Artificial Intelligence Nanodegree¶
Computer Vision Capstone¶
Project: Facial Keypoint Detection¶
Welcome to the final Computer Vision project in the Artificial Intelligence Nanodegree program!
In this project, you’ll combine your knowledge of computer vision techniques and deep learning to build and end-to-end facial keypoint recognition system! Facial keypoints include points around the eyes, nose, and mouth on any face and are used in many applications, from facial tracking to emotion recognition.
There are three main parts to this project:
Part 1 : Investigating OpenCV, pre-processing, and face detection
Part 2 : Training a Convolutional Neural Network (CNN) to detect facial keypoints
Part 3 : Putting parts 1 and 2 together to identify facial keypoints on any image!
*Here's what you need to know to complete the project:
In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested.
a. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!
In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation.
a. Each section where you will answer a question is preceded by a 'Question X' header.
b. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'.
Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.
The rubric contains optional suggestions for enhancing the project beyond the minimum requirements. If you decide to pursue the "(Optional)" sections, you should include the code in this IPython notebook.
Your project submission will be evaluated based on your answers to each of the questions and the code implementations you provide.
Steps to Complete the Project¶
Each part of the notebook is further broken down into separate steps. Feel free to use the links below to navigate the notebook.
In this project you will get to explore a few of the many computer vision algorithms built into the OpenCV library. This expansive computer vision library is now almost 20 years old and still growing!
The project itself is broken down into three large parts, then even further into separate steps. Make sure to read through each step, and complete any sections that begin with '(IMPLEMENTATION)' in the header; these implementation sections may contain multiple TODOs that will be marked in code. For convenience, we provide links to each of these steps below.
Part 1 : Investigating OpenCV, pre-processing, and face detection
- Step 0: Detect Faces Using a Haar Cascade Classifier
- Step 1: Add Eye Detection
- Step 2: De-noise an Image for Better Face Detection
- Step 3: Blur an Image and Perform Edge Detection
- Step 4: Automatically Hide the Identity of an Individual
Part 2 : Training a Convolutional Neural Network (CNN) to detect facial keypoints
- Step 5: Create a CNN to Recognize Facial Keypoints
- Step 6: Compile and Train the Model
- Step 7: Visualize the Loss and Answer Questions
Part 3 : Putting parts 1 and 2 together to identify facial keypoints on any image!
- Step 8: Build a Robust Facial Keypoints Detector (Complete the CV Pipeline)
Step 0: Detect Faces Using a Haar Cascade Classifier¶
Have you ever wondered how Facebook automatically tags images with your friends' faces? Or how high-end cameras automatically find and focus on a certain person's face? Applications like these depend heavily on the machine learning task known as face detection - which is the task of automatically finding faces in images containing people.
At its root face detection is a classification problem - that is a problem of distinguishing between distinct classes of things. With face detection these distinct classes are 1) images of human faces and 2) everything else.
We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the detector_architectures directory.
Import Resources¶
In the next python cell, we load in the required libraries for this section of the project.
In [15]:
# Import required libraries for this section
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import math
import cv2 # OpenCV library for computer vision
from PIL import Image
import time
Next, we load in and display a test image for performing face detection.
Note: by default OpenCV assumes the ordering of our image's color channels are Blue, then Green, then Red. This is slightly out of order with most image types we'll use in these experiments, whose color channels are ordered Red, then Green, then Blue. In order to switch the Blue and Red channels of our test image around we will use OpenCV's cvtColor function, which you can read more about by checking out some of its documentation located here. This is a general utility function that can do other transformations too like converting a color image to grayscale, and transforming a standard color image to HSV color space.
In [16]:
# Load in color image for face detection
image = cv2.imread('images/test_image_1.jpg')
# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Plot our image using subplots to specify a size and title
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Original Image')
ax1.imshow(image)
Out[16]:
<matplotlib.image.AxesImage at 0x7fd65c55bb70>
There are a lot of people - and faces - in this picture. 13 faces to be exact! In the next code cell, we demonstrate how to use a Haar Cascade classifier to detect all the faces in this test image.
This face detector uses information about patterns of intensity in an image to reliably detect faces under varying light conditions. So, to use this face detector, we'll first convert the image from color to grayscale.
Then, we load in the fully trained architecture of the face detector -- found in the file haarcascade_frontalface_default.xml - and use it on our image to find faces!
To learn more about the parameters of the detector see this post.
In [17]:
# Convert the RGB image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')
# Detect the faces in image
faces = face_cascade.detectMultiScale(gray, 4, 6)
# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))
# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(image)
# Get the bounding box for each detected face
for (x,y,w,h) in faces:
# Add a red bounding box to the detections image
cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
# Display the image with the detections
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Image with Face Detections')
ax1.imshow(image_with_detections)
Number of faces detected: 13
Out[17]:
<matplotlib.image.AxesImage at 0x7fd65c50b0f0>
In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.
Step 1: Add Eye Detections¶
There are other pre-trained detectors available that use a Haar Cascade Classifier - including full human body detectors, license plate detectors, and more. A full list of the pre-trained architectures can be found here.
To test your eye detector, we'll first read in a new test image with just a single face.
In [18]:
# Load in color image for face detection
image = cv2.imread('images/james.jpg')
# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Plot the RGB image
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Original Image')
ax1.imshow(image)
Out[18]:
<matplotlib.image.AxesImage at 0x7fd65c52cd68>
Notice that even though the image is a black and white image, we have read it in as a color image and so it will still need to be converted to grayscale in order to perform the most accurate face detection.
So, the next steps will be to convert this image to grayscale, then load OpenCV's face detector and run it with parameters that detect this face accurately.
In [19]:
# Convert the RGB image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')
# Detect the faces in image
faces = face_cascade.detectMultiScale(gray, 1.25, 6)
# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))
# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(image)
# Get the bounding box for each detected face
for (x,y,w,h) in faces:
# Add a red bounding box to the detections image
cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
# Display the image with the detections
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Image with Face Detection')
ax1.imshow(image_with_detections)
Number of faces detected: 1
Out[19]:
<matplotlib.image.AxesImage at 0x7fd65c4d92e8>
(IMPLEMENTATION) Add an eye detector to the current face detection setup.¶
A Haar-cascade eye detector can be included in the same way that the face detector was and, in this first task, it will be your job to do just this.
To set up an eye detector, use the stored parameters of the eye cascade detector, called haarcascade_eye.xml, located in the detector_architectures subdirectory. In the next code cell, create your eye detector and store its detections.
A few notes before you get started:
First, make sure to give your loaded eye detector the variable name
eye_cascade
and give the list of eye regions you detect the variable name
eyes
Second, since we've already run the face detector over this image, you should only search for eyes within the rectangular face regions detected in faces. This will minimize false detections.
Lastly, once you've run your eye detector over the facial detection region, you should display the RGB image with both the face detection boxes (in red) and your eye detections (in green) to verify that everything works as expected.
In [20]:
# Make a copy of the original image to plot rectangle detections
image_with_detections = np.copy(image)
# Loop over the detections and draw their corresponding face detection boxes
for (x,y,w,h) in faces:
cv2.rectangle(image_with_detections, (x,y), (x+w,y+h),(255,0,0), 3)
# Do not change the code above this comment!
## TODO: Add eye detection, using haarcascade_eye.xml, to the current face detector algorithm
eye_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_eye.xml')
for (x,y,w,h) in faces:
cv2.rectangle(image_with_detections, (x,y), (x+w,y+h),(255,0,0), 3)
eyes = eye_cascade.detectMultiScale(gray[y:y+h, x:x+w])
for (ex,ey,ew,eh) in eyes:
cv2.rectangle(image_with_detections[y:y+h, x:x+w],(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
## TODO: Loop over the eye detections and draw their corresponding boxes in green on image_with_detections
# Plot the image with both faces and eyes detected
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Image with Face and Eye Detection')
ax1.imshow(image_with_detections)
Out[20]:
<matplotlib.image.AxesImage at 0x7fd65c481cf8>
(Optional) Add face and eye detection to your laptop camera¶
It's time to kick it up a notch, and add face and eye detection to your laptop's camera! Afterwards, you'll be able to show off your creation like in the gif shown below - made with a completed version of the code!
Notice that not all of the detections here are perfect - and your result need not be perfect either. You should spend a small amount of time tuning the parameters of your detectors to get reasonable results, but don't hold out for perfection. If we wanted perfection we'd need to spend a ton of time tuning the parameters of each detector, cleaning up the input image frames, etc. You can think of this as more of a rapid prototype.
The next cell contains code for a wrapper function called laptop_camera_face_eye_detector that, when called, will activate your laptop's camera. You will place the relevant face and eye detection code in this wrapper function to implement face/eye detection and mark those detections on each image frame that your camera captures.
Before adding anything to the function, you can run it to get an idea of how it works - a small window should pop up showing you the live feed from your camera; you can press any key to close this window.
Note: Mac users may find that activating this function kills the kernel of their notebook every once in a while. If this happens to you, just restart your notebook's kernel, activate cell(s) containing any crucial import statements, and you'll be good to go!
In [8]:
### Add face and eye detection to this laptop camera function
# Make sure to draw out all faces/eyes found in each frame on the shown video feed
import cv2
import time
# wrapper function for face/eye detection with your laptop camera
def laptop_camera_go():
# Create instance of video capturer
cv2.namedWindow("face detection activated")
vc = cv2.VideoCapture(0)
# Try to get the first frame
if vc.isOpened():
rval, frame = vc.read()
else:
rval = False
# Keep the video stream open
while rval:
# Plot the image from camera with all the face and eye detections marked
cv2.imshow("face detection activated", frame)
# Exit functionality - press any key to exit laptop video
key = cv2.waitKey(20)
if key > 0: # Exit by pressing any key
# Destroy windows
cv2.destroyAllWindows()
# Make sure window closes on OSx
for i in range (1,5):
cv2.waitKey(1)
return
# Read next frame
time.sleep(0.05) # control framerate for computation - default 20 frames per sec
rval, frame = vc.read()
In [9]:
# Call the laptop camera face/eye detector function above
laptop_camera_go()
--------------------------------------------------------------------------- error Traceback (most recent call last) <ipython-input-9-6947bd6b284f> in <module>() 1 # Call the laptop camera face/eye detector function above ----> 2 laptop_camera_go() <ipython-input-8-91aac62a8070> in laptop_camera_go() 8 def laptop_camera_go(): 9 # Create instance of video capturer ---> 10 cv2.namedWindow("face detection activated") 11 vc = cv2.VideoCapture(0) 12 error: /Users/travis/build/skvark/opencv-python/opencv/modules/highgui/src/window.cpp:565: error: (-2) The function is not implemented. Rebuild the library with Windows, GTK+ 2.x or Carbon support. If you are on Ubuntu or Debian, install libgtk2.0-dev and pkg-config, then re-run cmake or configure script in function cvNamedWindow
Image quality is an important aspect of any computer vision task. Typically, when creating a set of images to train a deep learning network, significant care is taken to ensure that training images are free of visual noise or artifacts that hinder object detection. While computer vision algorithms - like a face detector - are typically trained on 'nice' data such as this, new test data doesn't always look so nice!
When applying a trained computer vision algorithm to a new piece of test data one often cleans it up first before feeding it in. This sort of cleaning - referred to as pre-processing - can include a number of cleaning phases like blurring, de-noising, color transformations, etc., and many of these tasks can be accomplished using OpenCV.
In this short subsection we explore OpenCV's noise-removal functionality to see how we can clean up a noisy image, which we then feed into our trained face detector.
Create a noisy image to work with¶
In the next cell, we create an artificial noisy version of the previous multi-face image. This is a little exaggerated - we don't typically get images that are this noisy - but image noise, or 'grainy-ness' in a digitial image - is a fairly common phenomenon.
In [21]:
# Load in the multi-face test image again
image = cv2.imread('images/test_image_1.jpg')
# Convert the image copy to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Make an array copy of this image
image_with_noise = np.asarray(image)
# Create noise - here we add noise sampled randomly from a Gaussian distribution: a common model for noise
noise_level = 40
noise = np.random.randn(image.shape[0],image.shape[1],image.shape[2])*noise_level
# Add this noise to the array image copy
image_with_noise = image_with_noise + noise
# Convert back to uint8 format
image_with_noise = np.asarray([np.uint8(np.clip(i,0,255)) for i in image_with_noise])
# Plot our noisy image!
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Noisy Image')
ax1.imshow(image_with_noise)
Out[21]:
<matplotlib.image.AxesImage at 0x7fd65c4ab710>
In the context of face detection, the problem with an image like this is that - due to noise - we may miss some faces or get false detections.
In the next cell we apply the same trained OpenCV detector with the same settings as before, to see what sort of detections we get.
In [22]:
# Convert the RGB image to grayscale
gray_noise = cv2.cvtColor(image_with_noise, cv2.COLOR_RGB2GRAY)
# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')
# Detect the faces in image
faces = face_cascade.detectMultiScale(gray_noise, 4, 6)
# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))
# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(image_with_noise)
# Get the bounding box for each detected face
for (x,y,w,h) in faces:
# Add a red bounding box to the detections image
cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
# Display the image with the detections
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Noisy Image with Face Detections')
ax1.imshow(image_with_detections)
Number of faces detected: 11
Out[22]:
<matplotlib.image.AxesImage at 0x7fd65c450e80>
With this added noise we now miss one of the faces!
(IMPLEMENTATION) De-noise this image for better face detection¶
Time to get your hands dirty: using OpenCV's built in color image de-noising functionality called fastNlMeansDenoisingColored - de-noise this image enough so that all the faces in the image are properly detected. Once you have cleaned the image in the next cell, use the cell that follows to run our trained face detector over the cleaned image to check out its detections.
You can find its official documentation here and a useful example here.
Note: you can keep all parameters except photo_render fixed as shown in the second link above. Play around with the value of this parameter - see how it affects the resulting cleaned image.
In [23]:
## TODO: Use OpenCV's built in color image de-noising function to clean up our noisy image!
# Load in the multi-face test image again
denoised_image = cv2.fastNlMeansDenoisingColored(image_with_noise,None,19,70,7,21)
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('denoised')
ax1.imshow(denoised_image)
Out[23]:
<matplotlib.image.AxesImage at 0x7fd65c1c1a90>
In [27]:
faces = face_cascade.detectMultiScale(denoised_image, 1.25, 6)
print('Number of faces detected:', len(faces))
# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(denoised_image)
# Get the bounding box for each detected face
for (x,y,w,h) in faces:
# Add a red bounding box to the detections image
cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
# Display the image with the detections
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Cleaned with detections')
ax1.imshow(image_with_detections)
Number of faces detected: 13
Out[27]:
<matplotlib.image.AxesImage at 0x7fd65c14e470>
Now that we have developed a simple pipeline for detecting faces using OpenCV - let's start playing around with a few fun things we can do with all those detected faces!
Importance of Blur in Edge Detection¶
Edge detection is a concept that pops up almost everywhere in computer vision applications, as edge-based features (as well as features built on top of edges) are often some of the best features for e.g., object detection and recognition problems.
Edge detection is a dimension reduction technique - by keeping only the edges of an image we get to throw away a lot of non-discriminating information. And typically the most useful kind of edge-detection is one that preserves only the important, global structures (ignoring local structures that aren't very discriminative). So removing local structures / retaining global structures is a crucial pre-processing step to performing edge detection in an image, and blurring can do just that.
Below is an animated gif showing the result of an edge-detected cat taken from Wikipedia, where the image is gradually blurred more and more prior to edge detection. When the animation begins you can't quite make out what it's a picture of, but as the animation evolves and local structures are removed via blurring the cat becomes visible in the edge-detected image.
Edge detection is a convolution performed on the image itself, and you can read about Canny edge detection on this OpenCV documentation page.
Canny edge detection¶
In the cell below we load in a test image, then apply Canny edge detection on it. The original image is shown on the left panel of the figure, while the edge-detected version of the image is shown on the right. Notice how the result looks very busy - there are too many little details preserved in the image before it is sent to the edge detector. When applied in computer vision applications, edge detection should preserve global structure; doing away with local structures that don't help describe what objects are in the image.
In [28]:
# Load in the image
image = cv2.imread('images/fawzia.jpg')
# Convert to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Perform Canny edge detection
edges = cv2.Canny(gray,100,200)
# Dilate the image to amplify edges
edges = cv2.dilate(edges, None)
# Plot the RGB and edge-detected image
fig = plt.figure(figsize = (15,15))
ax1 = fig.add_subplot(121)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Original Image')
ax1.imshow(image)
ax2 = fig.add_subplot(122)
ax2.set_xticks([])
ax2.set_yticks([])
ax2.set_title('Canny Edges')
ax2.imshow(edges, cmap='gray')
Out[28]:
<matplotlib.image.AxesImage at 0x7fd65c11c320>
Without first blurring the image, and removing small, local structures, a lot of irrelevant edge content gets picked up and amplified by the detector (as shown in the right panel above).
(IMPLEMENTATION) Blur the image then perform edge detection¶
In the next cell, you will repeat this experiment - blurring the image first to remove these local structures, so that only the important boudnary details remain in the edge-detected image.
Blur the image by using OpenCV's filter2d functionality - which is discussed in this documentation page - and use an averaging kernel of width equal to 4.
In [29]:
### TODO: Blur the test imageusing OpenCV's filter2d functionality,
# Use an averaging kernel, and a kernel width equal to 4
kernel = np.ones((4,4),np.float32)/16
blurred = cv2.filter2D(image,-1,kernel)
## TODO: Then perform Canny edge detection and display the output
gray = cv2.cvtColor(blurred, cv2.COLOR_RGB2GRAY)
# Perform Canny edge detection
edges = cv2.Canny(gray,100,200)
# Dilate the image to amplify edges
edges = cv2.dilate(edges, None)
# Plot the RGB and edge-detected image
fig = plt.figure(figsize = (15,15))
ax1 = fig.add_subplot(121)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Original blurred Image')
ax1.imshow(image)
ax2 = fig.add_subplot(122)
ax2.set_xticks([])
ax2.set_yticks([])
ax2.set_title('Canny Edges')
ax2.imshow(edges, cmap='gray')
Out[29]:
<matplotlib.image.AxesImage at 0x7fd65c0e7d68>
Step 4: Automatically Hide the Identity of an Individual¶
If you film something like a documentary or reality TV, you must get permission from every individual shown on film before you can show their face, otherwise you need to blur it out - by blurring the face a lot (so much so that even the global structures are obscured)! This is also true for projects like Google's StreetView maps - an enormous collection of mapping images taken from a fleet of Google vehicles. Because it would be impossible for Google to get the permission of every single person accidentally captured in one of these images they blur out everyone's faces, the detected images must automatically blur the identity of detected people. Here's a few examples of folks caught in the camera of a Google street view vehicle.
Read in an image to perform identity detection¶
Let's try this out for ourselves. Use the face detection pipeline built above and what you know about using the filter2D to blur and image, and use these in tandem to hide the identity of the person in the following image - loaded in and printed in the next cell.
In [30]:
# Load in the image
image = cv2.imread('images/gus.jpg')
# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Display the image
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Original Image')
ax1.imshow(image)
Out[30]:
<matplotlib.image.AxesImage at 0x7fd65c092550>
(IMPLEMENTATION) Use blurring to hide the identity of an individual in an image¶
The idea here is to 1) automatically detect the face in this image, and then 2) blur it out! Make sure to adjust the parameters of the averaging blur filter to completely obscure this person's identity.
In [31]:
# Detect the faces in image
faces = face_cascade.detectMultiScale(image, 4, 6)
# Make a copy of the orginal image to draw face detections on
image_with_blurred = np.copy(image)
kernel = np.ones((128,128),np.float32)/128**2
for (x,y,w,h) in faces:
blurred = cv2.filter2D(image_with_blurred[y:y+h, x:x+w],-1,kernel)
image_with_blurred[y:y+h, x:x+w] = blurred
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Image with Blurred')
ax1.imshow(image_with_blurred)
Out[31]:
<matplotlib.image.AxesImage at 0x7fd65c0b8c88>
(Optional) Build identity protection into your laptop camera¶
In this optional task you can add identity protection to your laptop camera, using the previously completed code where you added face detection to your laptop camera - and the task above. You should be able to get reasonable results with little parameter tuning - like the one shown in the gif below.
As with the previous video task, to make this perfect would require significant effort - so don't strive for perfection here, strive for reasonable quality.
The next cell contains code a wrapper function called laptop_camera_identity_hider that - when called - will activate your laptop's camera. You need to place the relevant face detection and blurring code developed above in this function in order to blur faces entering your laptop camera's field of view.
Before adding anything to the function you can call it to get a hang of how it works - a small window will pop up showing you the live feed from your camera, you can press any key to close this window.
Note: Mac users may find that activating this function kills the kernel of their notebook every once in a while. If this happens to you, just restart your notebook's kernel, activate cell(s) containing any crucial import statements, and you'll be good to go!
In [32]:
### Insert face detection and blurring code into the wrapper below to create an identity protector on your laptop!
import cv2
import time
def laptop_camera_go():
# Create instance of video capturer
cv2.namedWindow("face detection activated")
vc = cv2.VideoCapture(0)
# Try to get the first frame
if vc.isOpened():
rval, frame = vc.read()
else:
rval = False
# Keep video stream open
while rval:
# Plot image from camera with detections marked
cv2.imshow("face detection activated", frame)
# Exit functionality - press any key to exit laptop video
key = cv2.waitKey(20)
if key > 0: # Exit by pressing any key
# Destroy windows
cv2.destroyAllWindows()
for i in range (1,5):
cv2.waitKey(1)
return
# Read next frame
time.sleep(0.05) # control framerate for computation - default 20 frames per sec
rval, frame = vc.read()
In [ ]:
# Run laptop identity hider
laptop_camera_go()
Step 5: Create a CNN to Recognize Facial Keypoints¶
OpenCV is often used in practice with other machine learning and deep learning libraries to produce interesting results. In this stage of the project you will create your own end-to-end pipeline - employing convolutional networks in keras along with OpenCV - to apply a "selfie" filter to streaming video and images.
You will start by creating and then training a convolutional network that can detect facial keypoints in a small dataset of cropped images of human faces. We then guide you towards OpenCV to expanding your detection algorithm to more general images. What are facial keypoints? Let's take a look at some examples.
Facial keypoints (also called facial landmarks) are the small blue-green dots shown on each of the faces in the image above - there are 15 keypoints marked in each image. They mark important areas of the face - the eyes, corners of the mouth, the nose, etc. Facial keypoints can be used in a variety of machine learning applications from face and emotion recognition to commercial applications like the image filters popularized by Snapchat.
Below we illustrate a filter that, using the results of this section, automatically places sunglasses on people in images (using the facial keypoints to place the glasses correctly on each face). Here, the facial keypoints have been colored lime green for visualization purposes.
Make a facial keypoint detector¶
But first things first: how can we make a facial keypoint detector? Well, at a high level, notice that facial keypoint detection is a regression problem. A single face corresponds to a set of 15 facial keypoints (a set of 15 corresponding $(x, y)$ coordinates, i.e., an output point). Because our input data are images, we can employ a convolutional neural network to recognize patterns in our images and learn how to identify these keypoint given sets of labeled data.
In order to train a regressor, we need a training set - a set of facial image / facial keypoint pairs to train on. For this we will be using this dataset from Kaggle. We've already downloaded this data and placed it in the data directory. Make sure that you have both the training and test data files. The training dataset contains several thousand $96 \times 96$ grayscale images of cropped human faces, along with each face's 15 corresponding facial keypoints (also called landmarks) that have been placed by hand, and recorded in $(x, y)$ coordinates. This wonderful resource also has a substantial testing set, which we will use in tinkering with our convolutional network.
To load in this data, run the Python cell below - notice we will load in both the training and testing sets.
The load_data function is in the included utils.py file.
In [33]:
from utils import *
# Load training set
X_train, y_train = load_data()
print("X_train.shape == {}".format(X_train.shape))
print("y_train.shape == {}; y_train.min == {:.3f}; y_train.max == {:.3f}".format(
y_train.shape, y_train.min(), y_train.max()))
# Load testing set
X_test, _ = load_data(test=True)
print("X_test.shape == {}".format(X_test.shape))
X_train.shape == (2140, 96, 96, 1) y_train.shape == (2140, 30); y_train.min == -0.920; y_train.max == 0.996 X_test.shape == (1783, 96, 96, 1)
The load_data function in utils.py originates from this excellent blog post, which you are strongly encouraged to read. Please take the time now to review this function. Note how the output values - that is, the coordinates of each set of facial landmarks - have been normalized to take on values in the range $[-1, 1]$, while the pixel values of each input point (a facial image) have been normalized to the range $[0,1]$.
Note: the original Kaggle dataset contains some images with several missing keypoints. For simplicity, the load_data function removes those images with missing labels from the dataset. As an optional extension, you are welcome to amend the load_data function to include the incomplete data points.
Visualize the Training Data¶
Execute the code cell below to visualize a subset of the training data.
In [2]:
import matplotlib.pyplot as plt
%matplotlib inline
fig = plt.figure(figsize=(20,20))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
for i in range(9):
ax = fig.add_subplot(3, 3, i + 1, xticks=[], yticks=[])
plot_data(X_train[i], y_train[i], ax)
For each training image, there are two landmarks per eyebrow (four total), three per eye (six total), four for the mouth, and one for the tip of the nose.
Review the plot_data function in utils.py to understand how the 30-dimensional training labels in y_train are mapped to facial locations, as this function will prove useful for your pipeline.
(IMPLEMENTATION) Specify the CNN Architecture¶
In this section, you will specify a neural network for predicting the locations of facial keypoints. Use the code cell below to specify the architecture of your neural network. We have imported some layers that you may find useful for this task, but if you need to use more Keras layers, feel free to import them in the cell.
Your network should accept a $96 \times 96$ grayscale image as input, and it should output a vector with 30 entries, corresponding to the predicted (horizontal and vertical) locations of 15 facial keypoints. If you are not sure where to start, you can find some useful starting architectures in this blog, but you are not permitted to copy any of the architectures that you find online.
In [3]:
# Import deep learning resources from Keras
from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D, Dropout
from keras.layers import GlobalAveragePooling2D, Dense
## TODO: Specify a CNN architecture
# Your model should accept 96x96 pixel graysale images in
# It should have a fully-connected output layer with 30 values (2 for each facial keypoint)
def build_model():
model = Sequential()
model.add(Convolution2D(filters=16, kernel_size=2, padding='valid', activation='relu', input_shape=X_train.shape[1:]))
model.add(Dropout(0.1))
model.add(Convolution2D(filters=16, kernel_size=2, padding='valid', activation='relu'))
model.add(Dropout(0.1))
model.add(Convolution2D(filters=16, kernel_size=2, padding='valid', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.1))
model.add(Convolution2D(filters=32, kernel_size=2, padding='same', activation='relu'))
model.add(Dropout(0.2))
model.add(MaxPooling2D(pool_size=2))
model.add(Convolution2D(filters=64, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
model.add(Convolution2D(filters=128, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
model.add(Convolution2D(filters=256, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
model.add(Convolution2D(filters=512, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
model.add(GlobalAveragePooling2D())
model.add(Dense(30))
# Summarize the model
#model.summary()
return model
Step 6: Compile and Train the Model¶
After specifying your architecture, you'll need to compile and train the model to detect facial keypoints'
(IMPLEMENTATION) Compile and Train the Model¶
Use the compile method to configure the learning process. Experiment with your choice of optimizer; you may have some ideas about which will work best (SGD vs. RMSprop, etc), but take the time to empirically verify your theories.
Use the fit method to train the model. Break off a validation set by setting validation_split=0.2. Save the returned History object in the history variable.
Experiment with your model to minimize the validation loss (measured as mean squared error). A very good model will achieve about 0.0015 loss (though it's possible to do even better). When you have finished training, save your model as an HDF5 file with file path my_model.h5.
In [4]:
from keras.optimizers import SGD, RMSprop, Adagrad, Adadelta, Adam, Adamax, Nadam
from keras.callbacks import ModelCheckpoint
opts = ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']
best_optimizer = None
best_optimizer_loss = 9999
results = {}
for opt in opts:
## TODO: Compile the model
model = build_model()
model.compile(optimizer=opt, loss="mse", metrics=['accuracy'])
## TODO: Save the model as model.h5
# Saving the best model
checkpointer = ModelCheckpoint(filepath='my_model_{}.h5'.format(opt),
verbose=1, save_best_only=True)
## TODO: Train the model
hist = model.fit(X_train, y_train, batch_size=32, epochs=250, verbose=1,
validation_split=0.2, callbacks=[checkpointer])
opt_loss = hist.history['val_loss'][-1]
print('{} loss = {}'.format(opt, opt_loss))
results.update({'hist_{}'.format(opt): hist})
Train on 1712 samples, validate on 428 samples Epoch 1/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.1246 - acc: 0.1268Epoch 00000: val_loss improved from inf to 0.12319, saving model to my_model_SGD.h5 1712/1712 [==============================] - 4s - loss: 0.1243 - acc: 0.1285 - val_loss: 0.1232 - val_acc: 0.6963 Epoch 2/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0648 - acc: 0.2406Epoch 00001: val_loss improved from 0.12319 to 0.08403, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0645 - acc: 0.2418 - val_loss: 0.0840 - val_acc: 0.6963 Epoch 3/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0252 - acc: 0.3779Epoch 00002: val_loss improved from 0.08403 to 0.06834, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0252 - acc: 0.3773 - val_loss: 0.0683 - val_acc: 0.6963 Epoch 4/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0175 - acc: 0.4363Epoch 00003: val_loss improved from 0.06834 to 0.06554, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0175 - acc: 0.4369 - val_loss: 0.0655 - val_acc: 0.6963 Epoch 5/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0154 - acc: 0.4640Epoch 00004: val_loss improved from 0.06554 to 0.06410, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0154 - acc: 0.4650 - val_loss: 0.0641 - val_acc: 0.6893 Epoch 6/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0145 - acc: 0.4953Epoch 00005: val_loss improved from 0.06410 to 0.06406, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0145 - acc: 0.4959 - val_loss: 0.0641 - val_acc: 0.6799 Epoch 7/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0139 - acc: 0.4847Epoch 00006: val_loss improved from 0.06406 to 0.06290, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0139 - acc: 0.4871 - val_loss: 0.0629 - val_acc: 0.6963 Epoch 8/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0133 - acc: 0.4900Epoch 00007: val_loss improved from 0.06290 to 0.06206, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0133 - acc: 0.4883 - val_loss: 0.0621 - val_acc: 0.6963 Epoch 9/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0128 - acc: 0.4982Epoch 00008: val_loss improved from 0.06206 to 0.06170, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0128 - acc: 0.4953 - val_loss: 0.0617 - val_acc: 0.6963 Epoch 10/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0123 - acc: 0.4988Epoch 00009: val_loss improved from 0.06170 to 0.06102, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0122 - acc: 0.5012 - val_loss: 0.0610 - val_acc: 0.6963 Epoch 11/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0120 - acc: 0.5065Epoch 00010: val_loss improved from 0.06102 to 0.06049, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0120 - acc: 0.5093 - val_loss: 0.0605 - val_acc: 0.6963 Epoch 12/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0116 - acc: 0.5142Epoch 00011: val_loss improved from 0.06049 to 0.05950, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0116 - acc: 0.5134 - val_loss: 0.0595 - val_acc: 0.6963 Epoch 13/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0114 - acc: 0.5112Epoch 00012: val_loss improved from 0.05950 to 0.05915, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0114 - acc: 0.5111 - val_loss: 0.0591 - val_acc: 0.6963 Epoch 14/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0111 - acc: 0.5230Epoch 00013: val_loss improved from 0.05915 to 0.05832, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0111 - acc: 0.5210 - val_loss: 0.0583 - val_acc: 0.6963 Epoch 15/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0108 - acc: 0.5318Epoch 00014: val_loss improved from 0.05832 to 0.05776, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0108 - acc: 0.5315 - val_loss: 0.0578 - val_acc: 0.6963 Epoch 16/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0107 - acc: 0.5236Epoch 00015: val_loss improved from 0.05776 to 0.05717, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0107 - acc: 0.5245 - val_loss: 0.0572 - val_acc: 0.6963 Epoch 17/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.5448Epoch 00016: val_loss did not improve 1712/1712 [==============================] - 2s - loss: 0.0105 - acc: 0.5450 - val_loss: 0.0573 - val_acc: 0.6963 Epoch 18/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.5460Epoch 00017: val_loss improved from 0.05717 to 0.05668, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0103 - acc: 0.5456 - val_loss: 0.0567 - val_acc: 0.6963 Epoch 19/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.5572Epoch 00018: val_loss improved from 0.05668 to 0.05599, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0101 - acc: 0.5578 - val_loss: 0.0560 - val_acc: 0.6963 Epoch 20/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.5436Epoch 00019: val_loss improved from 0.05599 to 0.05532, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0099 - acc: 0.5421 - val_loss: 0.0553 - val_acc: 0.6963 Epoch 21/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.5584Epoch 00020: val_loss improved from 0.05532 to 0.05477, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0097 - acc: 0.5590 - val_loss: 0.0548 - val_acc: 0.6963 Epoch 22/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.5560Epoch 00021: val_loss improved from 0.05477 to 0.05417, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0096 - acc: 0.5549 - val_loss: 0.0542 - val_acc: 0.6963 Epoch 23/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.5731Epoch 00022: val_loss improved from 0.05417 to 0.05379, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0094 - acc: 0.5730 - val_loss: 0.0538 - val_acc: 0.6963 Epoch 24/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.5778Epoch 00023: val_loss improved from 0.05379 to 0.05330, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0094 - acc: 0.5783 - val_loss: 0.0533 - val_acc: 0.6963 Epoch 25/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.5596Epoch 00024: val_loss improved from 0.05330 to 0.05273, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0093 - acc: 0.5596 - val_loss: 0.0527 - val_acc: 0.6963 Epoch 26/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0091 - acc: 0.5584Epoch 00025: val_loss improved from 0.05273 to 0.05234, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0091 - acc: 0.5590 - val_loss: 0.0523 - val_acc: 0.6963 Epoch 27/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0091 - acc: 0.5784Epoch 00026: val_loss improved from 0.05234 to 0.05178, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0091 - acc: 0.5783 - val_loss: 0.0518 - val_acc: 0.6963 Epoch 28/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0090 - acc: 0.5784Epoch 00027: val_loss improved from 0.05178 to 0.05111, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0090 - acc: 0.5794 - val_loss: 0.0511 - val_acc: 0.6963 Epoch 29/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0089 - acc: 0.5820Epoch 00028: val_loss improved from 0.05111 to 0.05087, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0089 - acc: 0.5806 - val_loss: 0.0509 - val_acc: 0.6963 Epoch 30/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0087 - acc: 0.5660Epoch 00029: val_loss improved from 0.05087 to 0.05029, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0087 - acc: 0.5654 - val_loss: 0.0503 - val_acc: 0.6963 Epoch 31/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0087 - acc: 0.5926Epoch 00030: val_loss improved from 0.05029 to 0.04989, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0087 - acc: 0.5929 - val_loss: 0.0499 - val_acc: 0.6963 Epoch 32/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0085 - acc: 0.5991Epoch 00031: val_loss improved from 0.04989 to 0.04950, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0085 - acc: 0.5999 - val_loss: 0.0495 - val_acc: 0.6963 Epoch 33/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0084 - acc: 0.5908Epoch 00032: val_loss improved from 0.04950 to 0.04916, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0084 - acc: 0.5905 - val_loss: 0.0492 - val_acc: 0.6963 Epoch 34/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0084 - acc: 0.5825Epoch 00033: val_loss improved from 0.04916 to 0.04890, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0085 - acc: 0.5835 - val_loss: 0.0489 - val_acc: 0.6963 Epoch 35/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0084 - acc: 0.5973Epoch 00034: val_loss improved from 0.04890 to 0.04858, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0084 - acc: 0.5987 - val_loss: 0.0486 - val_acc: 0.6963 Epoch 36/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0082 - acc: 0.5767Epoch 00035: val_loss improved from 0.04858 to 0.04766, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0082 - acc: 0.5783 - val_loss: 0.0477 - val_acc: 0.6963 Epoch 37/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0081 - acc: 0.5837Epoch 00036: val_loss improved from 0.04766 to 0.04736, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0081 - acc: 0.5847 - val_loss: 0.0474 - val_acc: 0.6963 Epoch 38/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0082 - acc: 0.5784Epoch 00037: val_loss improved from 0.04736 to 0.04695, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0082 - acc: 0.5806 - val_loss: 0.0470 - val_acc: 0.6963 Epoch 39/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0081 - acc: 0.5914Epoch 00038: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0081 - acc: 0.5911 - val_loss: 0.0470 - val_acc: 0.6963 Epoch 40/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0080 - acc: 0.5943Epoch 00039: val_loss improved from 0.04695 to 0.04642, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0080 - acc: 0.5946 - val_loss: 0.0464 - val_acc: 0.6963 Epoch 41/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0079 - acc: 0.5943Epoch 00040: val_loss improved from 0.04642 to 0.04626, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0080 - acc: 0.5946 - val_loss: 0.0463 - val_acc: 0.6963 Epoch 42/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0079 - acc: 0.6032Epoch 00041: val_loss improved from 0.04626 to 0.04587, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0079 - acc: 0.6028 - val_loss: 0.0459 - val_acc: 0.6963 Epoch 43/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0079 - acc: 0.5991Epoch 00042: val_loss improved from 0.04587 to 0.04552, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0079 - acc: 0.5999 - val_loss: 0.0455 - val_acc: 0.6963 Epoch 44/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0078 - acc: 0.5961Epoch 00043: val_loss improved from 0.04552 to 0.04534, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0078 - acc: 0.5952 - val_loss: 0.0453 - val_acc: 0.6963 Epoch 45/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0079 - acc: 0.6026Epoch 00044: val_loss improved from 0.04534 to 0.04489, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0079 - acc: 0.6022 - val_loss: 0.0449 - val_acc: 0.6963 Epoch 46/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0078 - acc: 0.5849Epoch 00045: val_loss improved from 0.04489 to 0.04434, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0078 - acc: 0.5841 - val_loss: 0.0443 - val_acc: 0.6963 Epoch 47/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0077 - acc: 0.6079Epoch 00046: val_loss improved from 0.04434 to 0.04415, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0077 - acc: 0.6081 - val_loss: 0.0442 - val_acc: 0.6963 Epoch 48/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0076 - acc: 0.6002Epoch 00047: val_loss improved from 0.04415 to 0.04393, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0076 - acc: 0.6011 - val_loss: 0.0439 - val_acc: 0.6963 Epoch 49/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0077 - acc: 0.5973Epoch 00048: val_loss improved from 0.04393 to 0.04350, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0077 - acc: 0.5964 - val_loss: 0.0435 - val_acc: 0.6963 Epoch 50/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0076 - acc: 0.6020Epoch 00049: val_loss improved from 0.04350 to 0.04296, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0076 - acc: 0.6016 - val_loss: 0.0430 - val_acc: 0.6963 Epoch 51/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0076 - acc: 0.6108Epoch 00050: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0076 - acc: 0.6121 - val_loss: 0.0432 - val_acc: 0.6963 Epoch 52/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0075 - acc: 0.6126Epoch 00051: val_loss improved from 0.04296 to 0.04257, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0075 - acc: 0.6110 - val_loss: 0.0426 - val_acc: 0.6963 Epoch 53/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0075 - acc: 0.6120Epoch 00052: val_loss improved from 0.04257 to 0.04238, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0075 - acc: 0.6121 - val_loss: 0.0424 - val_acc: 0.6963 Epoch 54/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0074 - acc: 0.5991Epoch 00053: val_loss improved from 0.04238 to 0.04203, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0074 - acc: 0.5999 - val_loss: 0.0420 - val_acc: 0.6963 Epoch 55/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0074 - acc: 0.6132Epoch 00054: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0074 - acc: 0.6145 - val_loss: 0.0421 - val_acc: 0.6963 Epoch 56/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0074 - acc: 0.6274Epoch 00055: val_loss improved from 0.04203 to 0.04161, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0074 - acc: 0.6279 - val_loss: 0.0416 - val_acc: 0.6963 Epoch 57/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0074 - acc: 0.6297Epoch 00056: val_loss improved from 0.04161 to 0.04145, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0074 - acc: 0.6297 - val_loss: 0.0414 - val_acc: 0.6963 Epoch 58/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0073 - acc: 0.6221Epoch 00057: val_loss improved from 0.04145 to 0.04092, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0073 - acc: 0.6197 - val_loss: 0.0409 - val_acc: 0.6963 Epoch 59/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0073 - acc: 0.6250Epoch 00058: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0073 - acc: 0.6238 - val_loss: 0.0409 - val_acc: 0.6963 Epoch 60/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0073 - acc: 0.6297Epoch 00059: val_loss improved from 0.04092 to 0.04059, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0073 - acc: 0.6303 - val_loss: 0.0406 - val_acc: 0.6963 Epoch 61/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0072 - acc: 0.6126Epoch 00060: val_loss improved from 0.04059 to 0.04040, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0072 - acc: 0.6110 - val_loss: 0.0404 - val_acc: 0.6963 Epoch 62/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0072 - acc: 0.6114Epoch 00061: val_loss improved from 0.04040 to 0.04031, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0072 - acc: 0.6110 - val_loss: 0.0403 - val_acc: 0.6963 Epoch 63/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0072 - acc: 0.6103Epoch 00062: val_loss improved from 0.04031 to 0.03990, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0072 - acc: 0.6092 - val_loss: 0.0399 - val_acc: 0.6963 Epoch 64/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0071 - acc: 0.6108Epoch 00063: val_loss improved from 0.03990 to 0.03957, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0071 - acc: 0.6092 - val_loss: 0.0396 - val_acc: 0.6963 Epoch 65/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0071 - acc: 0.6203Epoch 00064: val_loss improved from 0.03957 to 0.03937, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0071 - acc: 0.6203 - val_loss: 0.0394 - val_acc: 0.6963 Epoch 66/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0071 - acc: 0.6144Epoch 00065: val_loss improved from 0.03937 to 0.03912, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0071 - acc: 0.6139 - val_loss: 0.0391 - val_acc: 0.6963 Epoch 67/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0072 - acc: 0.6067Epoch 00066: val_loss improved from 0.03912 to 0.03891, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0072 - acc: 0.6069 - val_loss: 0.0389 - val_acc: 0.6963 Epoch 68/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0072 - acc: 0.6173Epoch 00067: val_loss improved from 0.03891 to 0.03861, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0072 - acc: 0.6180 - val_loss: 0.0386 - val_acc: 0.6963 Epoch 69/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0071 - acc: 0.6091Epoch 00068: val_loss improved from 0.03861 to 0.03857, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0071 - acc: 0.6081 - val_loss: 0.0386 - val_acc: 0.6963 Epoch 70/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0070 - acc: 0.6203Epoch 00069: val_loss improved from 0.03857 to 0.03818, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0070 - acc: 0.6203 - val_loss: 0.0382 - val_acc: 0.6963 Epoch 71/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0069 - acc: 0.6108Epoch 00070: val_loss improved from 0.03818 to 0.03813, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0069 - acc: 0.6081 - val_loss: 0.0381 - val_acc: 0.6963 Epoch 72/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0070 - acc: 0.6279Epoch 00071: val_loss improved from 0.03813 to 0.03804, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0070 - acc: 0.6279 - val_loss: 0.0380 - val_acc: 0.6963 Epoch 73/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0069 - acc: 0.6309Epoch 00072: val_loss improved from 0.03804 to 0.03784, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0069 - acc: 0.6279 - val_loss: 0.0378 - val_acc: 0.6963 Epoch 74/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0069 - acc: 0.6468Epoch 00073: val_loss improved from 0.03784 to 0.03752, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0069 - acc: 0.6472 - val_loss: 0.0375 - val_acc: 0.6963 Epoch 75/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0069 - acc: 0.6203Epoch 00074: val_loss improved from 0.03752 to 0.03731, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0069 - acc: 0.6197 - val_loss: 0.0373 - val_acc: 0.6963 Epoch 76/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0068 - acc: 0.6232Epoch 00075: val_loss improved from 0.03731 to 0.03718, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0068 - acc: 0.6232 - val_loss: 0.0372 - val_acc: 0.6963 Epoch 77/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0069 - acc: 0.6315Epoch 00076: val_loss improved from 0.03718 to 0.03692, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0069 - acc: 0.6320 - val_loss: 0.0369 - val_acc: 0.6963 Epoch 78/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0068 - acc: 0.6403Epoch 00077: val_loss improved from 0.03692 to 0.03659, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0068 - acc: 0.6390 - val_loss: 0.0366 - val_acc: 0.6963 Epoch 79/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0068 - acc: 0.6297Epoch 00078: val_loss improved from 0.03659 to 0.03645, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0068 - acc: 0.6297 - val_loss: 0.0364 - val_acc: 0.6963 Epoch 80/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0069 - acc: 0.6256Epoch 00079: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0069 - acc: 0.6250 - val_loss: 0.0365 - val_acc: 0.6963 Epoch 81/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0068 - acc: 0.6268Epoch 00080: val_loss improved from 0.03645 to 0.03621, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0068 - acc: 0.6268 - val_loss: 0.0362 - val_acc: 0.6963 Epoch 82/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0067 - acc: 0.6338Epoch 00081: val_loss improved from 0.03621 to 0.03602, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0067 - acc: 0.6332 - val_loss: 0.0360 - val_acc: 0.6963 Epoch 83/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0069 - acc: 0.6297Epoch 00082: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0068 - acc: 0.6297 - val_loss: 0.0361 - val_acc: 0.6963 Epoch 84/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0067 - acc: 0.6215Epoch 00083: val_loss improved from 0.03602 to 0.03543, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0067 - acc: 0.6227 - val_loss: 0.0354 - val_acc: 0.6963 Epoch 85/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0068 - acc: 0.6226Epoch 00084: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0068 - acc: 0.6215 - val_loss: 0.0355 - val_acc: 0.6963 Epoch 86/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0067 - acc: 0.6303Epoch 00085: val_loss improved from 0.03543 to 0.03527, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0067 - acc: 0.6320 - val_loss: 0.0353 - val_acc: 0.6963 Epoch 87/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0068 - acc: 0.6533Epoch 00086: val_loss improved from 0.03527 to 0.03499, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0068 - acc: 0.6530 - val_loss: 0.0350 - val_acc: 0.6963 Epoch 88/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0067 - acc: 0.6338Epoch 00087: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0067 - acc: 0.6355 - val_loss: 0.0352 - val_acc: 0.6963 Epoch 89/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0067 - acc: 0.6338Epoch 00088: val_loss improved from 0.03499 to 0.03475, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0067 - acc: 0.6343 - val_loss: 0.0347 - val_acc: 0.6963 Epoch 90/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0066 - acc: 0.6315Epoch 00089: val_loss did not improve 1712/1712 [==============================] - 2s - loss: 0.0066 - acc: 0.6320 - val_loss: 0.0348 - val_acc: 0.6963 Epoch 91/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0066 - acc: 0.6415Epoch 00090: val_loss improved from 0.03475 to 0.03461, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0066 - acc: 0.6402 - val_loss: 0.0346 - val_acc: 0.6963 Epoch 92/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0067 - acc: 0.6397Epoch 00091: val_loss improved from 0.03461 to 0.03424, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0067 - acc: 0.6396 - val_loss: 0.0342 - val_acc: 0.6963 Epoch 93/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0066 - acc: 0.6344Epoch 00092: val_loss improved from 0.03424 to 0.03422, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0066 - acc: 0.6343 - val_loss: 0.0342 - val_acc: 0.6963 Epoch 94/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0066 - acc: 0.6327Epoch 00093: val_loss improved from 0.03422 to 0.03403, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0066 - acc: 0.6332 - val_loss: 0.0340 - val_acc: 0.6963 Epoch 95/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0066 - acc: 0.6474Epoch 00094: val_loss improved from 0.03403 to 0.03388, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0066 - acc: 0.6460 - val_loss: 0.0339 - val_acc: 0.6963 Epoch 96/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0066 - acc: 0.6309Epoch 00095: val_loss improved from 0.03388 to 0.03365, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0066 - acc: 0.6332 - val_loss: 0.0336 - val_acc: 0.6963 Epoch 97/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0066 - acc: 0.6403Epoch 00096: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0066 - acc: 0.6414 - val_loss: 0.0338 - val_acc: 0.6963 Epoch 98/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0065 - acc: 0.6397Epoch 00097: val_loss improved from 0.03365 to 0.03363, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0065 - acc: 0.6402 - val_loss: 0.0336 - val_acc: 0.6963 Epoch 99/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0066 - acc: 0.6392Epoch 00098: val_loss improved from 0.03363 to 0.03331, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0066 - acc: 0.6390 - val_loss: 0.0333 - val_acc: 0.6963 Epoch 100/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0065 - acc: 0.6409Epoch 00099: val_loss improved from 0.03331 to 0.03315, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0065 - acc: 0.6414 - val_loss: 0.0332 - val_acc: 0.6963 Epoch 101/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0065 - acc: 0.6533Epoch 00100: val_loss improved from 0.03315 to 0.03302, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0065 - acc: 0.6548 - val_loss: 0.0330 - val_acc: 0.6963 Epoch 102/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0065 - acc: 0.6380Epoch 00101: val_loss improved from 0.03302 to 0.03283, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0065 - acc: 0.6373 - val_loss: 0.0328 - val_acc: 0.6963 Epoch 103/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0065 - acc: 0.6403Epoch 00102: val_loss improved from 0.03283 to 0.03274, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0065 - acc: 0.6402 - val_loss: 0.0327 - val_acc: 0.6963 Epoch 104/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0065 - acc: 0.6450Epoch 00103: val_loss improved from 0.03274 to 0.03238, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0065 - acc: 0.6472 - val_loss: 0.0324 - val_acc: 0.6963 Epoch 105/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0065 - acc: 0.6486Epoch 00104: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0065 - acc: 0.6460 - val_loss: 0.0326 - val_acc: 0.6963 Epoch 106/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0065 - acc: 0.6462Epoch 00105: val_loss improved from 0.03238 to 0.03233, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0065 - acc: 0.6472 - val_loss: 0.0323 - val_acc: 0.6963 Epoch 107/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0065 - acc: 0.6403Epoch 00106: val_loss improved from 0.03233 to 0.03216, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0065 - acc: 0.6396 - val_loss: 0.0322 - val_acc: 0.6963 Epoch 108/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0064 - acc: 0.6456Epoch 00107: val_loss improved from 0.03216 to 0.03196, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0064 - acc: 0.6454 - val_loss: 0.0320 - val_acc: 0.6963 Epoch 109/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0065 - acc: 0.6521Epoch 00108: val_loss did not improve 1712/1712 [==============================] - 2s - loss: 0.0065 - acc: 0.6530 - val_loss: 0.0321 - val_acc: 0.6963 Epoch 110/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0064 - acc: 0.6421Epoch 00109: val_loss improved from 0.03196 to 0.03186, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0064 - acc: 0.6419 - val_loss: 0.0319 - val_acc: 0.6963 Epoch 111/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0064 - acc: 0.6409Epoch 00110: val_loss improved from 0.03186 to 0.03144, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0064 - acc: 0.6414 - val_loss: 0.0314 - val_acc: 0.6963 Epoch 112/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0064 - acc: 0.6327Epoch 00111: val_loss improved from 0.03144 to 0.03123, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0064 - acc: 0.6326 - val_loss: 0.0312 - val_acc: 0.6963 Epoch 113/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0065 - acc: 0.6521Epoch 00112: val_loss did not improve 1712/1712 [==============================] - 2s - loss: 0.0064 - acc: 0.6495 - val_loss: 0.0315 - val_acc: 0.6963 Epoch 114/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0064 - acc: 0.6421Epoch 00113: val_loss improved from 0.03123 to 0.03113, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0064 - acc: 0.6414 - val_loss: 0.0311 - val_acc: 0.6963 Epoch 115/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0063 - acc: 0.6386Epoch 00114: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0063 - acc: 0.6402 - val_loss: 0.0313 - val_acc: 0.6963 Epoch 116/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0064 - acc: 0.6456Epoch 00115: val_loss improved from 0.03113 to 0.03094, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0064 - acc: 0.6460 - val_loss: 0.0309 - val_acc: 0.6963 Epoch 117/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0064 - acc: 0.6574Epoch 00116: val_loss improved from 0.03094 to 0.03069, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0064 - acc: 0.6577 - val_loss: 0.0307 - val_acc: 0.6963 Epoch 118/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0063 - acc: 0.6504Epoch 00117: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0063 - acc: 0.6513 - val_loss: 0.0308 - val_acc: 0.6963 Epoch 119/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0064 - acc: 0.6598Epoch 00118: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0063 - acc: 0.6595 - val_loss: 0.0309 - val_acc: 0.6963 Epoch 120/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0063 - acc: 0.6527Epoch 00119: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0063 - acc: 0.6513 - val_loss: 0.0308 - val_acc: 0.6963 Epoch 121/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0063 - acc: 0.6521Epoch 00120: val_loss improved from 0.03069 to 0.03046, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0063 - acc: 0.6513 - val_loss: 0.0305 - val_acc: 0.6963 Epoch 122/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0064 - acc: 0.6327Epoch 00121: val_loss improved from 0.03046 to 0.03036, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0063 - acc: 0.6338 - val_loss: 0.0304 - val_acc: 0.6963 Epoch 123/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0063 - acc: 0.6580Epoch 00122: val_loss improved from 0.03036 to 0.03031, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0063 - acc: 0.6583 - val_loss: 0.0303 - val_acc: 0.6963 Epoch 124/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6521Epoch 00123: val_loss improved from 0.03031 to 0.03005, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6519 - val_loss: 0.0301 - val_acc: 0.6963 Epoch 125/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0063 - acc: 0.6439Epoch 00124: val_loss improved from 0.03005 to 0.02988, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0063 - acc: 0.6449 - val_loss: 0.0299 - val_acc: 0.6963 Epoch 126/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0063 - acc: 0.6492Epoch 00125: val_loss improved from 0.02988 to 0.02972, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0063 - acc: 0.6495 - val_loss: 0.0297 - val_acc: 0.6963 Epoch 127/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0063 - acc: 0.6568Epoch 00126: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0063 - acc: 0.6583 - val_loss: 0.0298 - val_acc: 0.6963 Epoch 128/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0063 - acc: 0.6409Epoch 00127: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6431 - val_loss: 0.0298 - val_acc: 0.6963 Epoch 129/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6574Epoch 00128: val_loss improved from 0.02972 to 0.02956, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6565 - val_loss: 0.0296 - val_acc: 0.6963 Epoch 130/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0063 - acc: 0.6450Epoch 00129: val_loss improved from 0.02956 to 0.02922, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6454 - val_loss: 0.0292 - val_acc: 0.6963 Epoch 131/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6509Epoch 00130: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6507 - val_loss: 0.0294 - val_acc: 0.6963 Epoch 132/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6386Epoch 00131: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6390 - val_loss: 0.0292 - val_acc: 0.6963 Epoch 133/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6445Epoch 00132: val_loss improved from 0.02922 to 0.02920, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6443 - val_loss: 0.0292 - val_acc: 0.6963 Epoch 134/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6657Epoch 00133: val_loss improved from 0.02920 to 0.02891, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6653 - val_loss: 0.0289 - val_acc: 0.6963 Epoch 135/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6574Epoch 00134: val_loss improved from 0.02891 to 0.02880, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6577 - val_loss: 0.0288 - val_acc: 0.6963 Epoch 136/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6669Epoch 00135: val_loss improved from 0.02880 to 0.02874, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6682 - val_loss: 0.0287 - val_acc: 0.6963 Epoch 137/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6533Epoch 00136: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6519 - val_loss: 0.0289 - val_acc: 0.6963 Epoch 138/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6592Epoch 00137: val_loss improved from 0.02874 to 0.02868, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6571 - val_loss: 0.0287 - val_acc: 0.6963 Epoch 139/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6421Epoch 00138: val_loss improved from 0.02868 to 0.02834, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6431 - val_loss: 0.0283 - val_acc: 0.6963 Epoch 140/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6515Epoch 00139: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6530 - val_loss: 0.0286 - val_acc: 0.6963 Epoch 141/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0061 - acc: 0.6450Epoch 00140: val_loss improved from 0.02834 to 0.02815, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0061 - acc: 0.6449 - val_loss: 0.0281 - val_acc: 0.6963 Epoch 142/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0061 - acc: 0.6574Epoch 00141: val_loss improved from 0.02815 to 0.02815, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6571 - val_loss: 0.0281 - val_acc: 0.6963 Epoch 143/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0061 - acc: 0.6633Epoch 00142: val_loss improved from 0.02815 to 0.02792, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0061 - acc: 0.6618 - val_loss: 0.0279 - val_acc: 0.6963 Epoch 144/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6421Epoch 00143: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6390 - val_loss: 0.0279 - val_acc: 0.6963 Epoch 145/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6562Epoch 00144: val_loss improved from 0.02792 to 0.02785, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6560 - val_loss: 0.0278 - val_acc: 0.6963 Epoch 146/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6515Epoch 00145: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6530 - val_loss: 0.0280 - val_acc: 0.6963 Epoch 147/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6598Epoch 00146: val_loss improved from 0.02785 to 0.02764, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6606 - val_loss: 0.0276 - val_acc: 0.6963 Epoch 148/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6498Epoch 00147: val_loss improved from 0.02764 to 0.02737, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6495 - val_loss: 0.0274 - val_acc: 0.6963 Epoch 149/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0061 - acc: 0.6604Epoch 00148: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0061 - acc: 0.6606 - val_loss: 0.0275 - val_acc: 0.6963 Epoch 150/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0061 - acc: 0.6698Epoch 00149: val_loss improved from 0.02737 to 0.02736, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0061 - acc: 0.6700 - val_loss: 0.0274 - val_acc: 0.6963 Epoch 151/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0061 - acc: 0.6804Epoch 00150: val_loss improved from 0.02736 to 0.02716, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0061 - acc: 0.6817 - val_loss: 0.0272 - val_acc: 0.6963 Epoch 152/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6551Epoch 00151: val_loss improved from 0.02716 to 0.02693, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6530 - val_loss: 0.0269 - val_acc: 0.6963 Epoch 153/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0061 - acc: 0.6669Epoch 00152: val_loss did not improve 1712/1712 [==============================] - 2s - loss: 0.0061 - acc: 0.6665 - val_loss: 0.0271 - val_acc: 0.6963 Epoch 154/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0061 - acc: 0.6409Epoch 00153: val_loss improved from 0.02693 to 0.02686, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0061 - acc: 0.6419 - val_loss: 0.0269 - val_acc: 0.6963 Epoch 155/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0061 - acc: 0.6533Epoch 00154: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0061 - acc: 0.6542 - val_loss: 0.0269 - val_acc: 0.6963 Epoch 156/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0061 - acc: 0.6498Epoch 00155: val_loss improved from 0.02686 to 0.02681, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0061 - acc: 0.6501 - val_loss: 0.0268 - val_acc: 0.6963 Epoch 157/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0061 - acc: 0.6616Epoch 00156: val_loss improved from 0.02681 to 0.02671, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0061 - acc: 0.6618 - val_loss: 0.0267 - val_acc: 0.6963 Epoch 158/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6551Epoch 00157: val_loss improved from 0.02671 to 0.02657, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6554 - val_loss: 0.0266 - val_acc: 0.6963 Epoch 159/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0061 - acc: 0.6716Epoch 00158: val_loss improved from 0.02657 to 0.02608, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0061 - acc: 0.6723 - val_loss: 0.0261 - val_acc: 0.6963 Epoch 160/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0061 - acc: 0.6557Epoch 00159: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0061 - acc: 0.6560 - val_loss: 0.0265 - val_acc: 0.6963 Epoch 161/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6698Epoch 00160: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6688 - val_loss: 0.0263 - val_acc: 0.6963 Epoch 162/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6621Epoch 00161: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6636 - val_loss: 0.0265 - val_acc: 0.6963 Epoch 163/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6527Epoch 00162: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6530 - val_loss: 0.0262 - val_acc: 0.6963 Epoch 164/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6610Epoch 00163: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6606 - val_loss: 0.0262 - val_acc: 0.6963 Epoch 165/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6610Epoch 00164: val_loss improved from 0.02608 to 0.02593, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0061 - acc: 0.6589 - val_loss: 0.0259 - val_acc: 0.6963 Epoch 166/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6621Epoch 00165: val_loss improved from 0.02593 to 0.02591, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6612 - val_loss: 0.0259 - val_acc: 0.6963 Epoch 167/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6539Epoch 00166: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6548 - val_loss: 0.0260 - val_acc: 0.6963 Epoch 168/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6627Epoch 00167: val_loss improved from 0.02591 to 0.02573, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6630 - val_loss: 0.0257 - val_acc: 0.6963 Epoch 169/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6728Epoch 00168: val_loss improved from 0.02573 to 0.02569, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6723 - val_loss: 0.0257 - val_acc: 0.6963 Epoch 170/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6604Epoch 00169: val_loss improved from 0.02569 to 0.02557, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6589 - val_loss: 0.0256 - val_acc: 0.6963 Epoch 171/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6651Epoch 00170: val_loss improved from 0.02557 to 0.02554, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6636 - val_loss: 0.0255 - val_acc: 0.6963 Epoch 172/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6686Epoch 00171: val_loss improved from 0.02554 to 0.02532, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6694 - val_loss: 0.0253 - val_acc: 0.6963 Epoch 173/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6557Epoch 00172: val_loss improved from 0.02532 to 0.02530, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6571 - val_loss: 0.0253 - val_acc: 0.6963 Epoch 174/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6745Epoch 00173: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6746 - val_loss: 0.0254 - val_acc: 0.6963 Epoch 175/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6680Epoch 00174: val_loss improved from 0.02530 to 0.02527, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6665 - val_loss: 0.0253 - val_acc: 0.6963 Epoch 176/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6527Epoch 00175: val_loss improved from 0.02527 to 0.02515, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6542 - val_loss: 0.0252 - val_acc: 0.6963 Epoch 177/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6686Epoch 00176: val_loss improved from 0.02515 to 0.02496, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6688 - val_loss: 0.0250 - val_acc: 0.6963 Epoch 178/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6616Epoch 00177: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6612 - val_loss: 0.0251 - val_acc: 0.6963 Epoch 179/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6651Epoch 00178: val_loss improved from 0.02496 to 0.02468, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6641 - val_loss: 0.0247 - val_acc: 0.6963 Epoch 180/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6598Epoch 00179: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6589 - val_loss: 0.0249 - val_acc: 0.6963 Epoch 181/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6545Epoch 00180: val_loss improved from 0.02468 to 0.02445, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6560 - val_loss: 0.0245 - val_acc: 0.6963 Epoch 182/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6580Epoch 00181: val_loss improved from 0.02445 to 0.02445, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6583 - val_loss: 0.0245 - val_acc: 0.6963 Epoch 183/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6804Epoch 00182: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6817 - val_loss: 0.0246 - val_acc: 0.6963 Epoch 184/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6562Epoch 00183: val_loss improved from 0.02445 to 0.02438, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6571 - val_loss: 0.0244 - val_acc: 0.6963 Epoch 185/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6592Epoch 00184: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6612 - val_loss: 0.0244 - val_acc: 0.6963 Epoch 186/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6663Epoch 00185: val_loss improved from 0.02438 to 0.02417, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6671 - val_loss: 0.0242 - val_acc: 0.6963 Epoch 187/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6675Epoch 00186: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6671 - val_loss: 0.0242 - val_acc: 0.6963 Epoch 188/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6645Epoch 00187: val_loss improved from 0.02417 to 0.02411, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6641 - val_loss: 0.0241 - val_acc: 0.6963 Epoch 189/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6651Epoch 00188: val_loss improved from 0.02411 to 0.02404, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6647 - val_loss: 0.0240 - val_acc: 0.6963 Epoch 190/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6598Epoch 00189: val_loss improved from 0.02404 to 0.02398, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6600 - val_loss: 0.0240 - val_acc: 0.6963 Epoch 191/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6675Epoch 00190: val_loss improved from 0.02398 to 0.02385, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6694 - val_loss: 0.0238 - val_acc: 0.6963 Epoch 192/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6710Epoch 00191: val_loss improved from 0.02385 to 0.02384, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6706 - val_loss: 0.0238 - val_acc: 0.6963 Epoch 193/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6545Epoch 00192: val_loss improved from 0.02384 to 0.02369, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6565 - val_loss: 0.0237 - val_acc: 0.6963 Epoch 194/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6781Epoch 00193: val_loss improved from 0.02369 to 0.02366, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6776 - val_loss: 0.0237 - val_acc: 0.6963 Epoch 195/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6775Epoch 00194: val_loss improved from 0.02366 to 0.02363, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6752 - val_loss: 0.0236 - val_acc: 0.6963 Epoch 196/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6627Epoch 00195: val_loss improved from 0.02363 to 0.02355, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6636 - val_loss: 0.0236 - val_acc: 0.6963 Epoch 197/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6616Epoch 00196: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6612 - val_loss: 0.0236 - val_acc: 0.6963 Epoch 198/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6804Epoch 00197: val_loss improved from 0.02355 to 0.02354, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6805 - val_loss: 0.0235 - val_acc: 0.6963 Epoch 199/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6710Epoch 00198: val_loss improved from 0.02354 to 0.02333, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6706 - val_loss: 0.0233 - val_acc: 0.6963 Epoch 200/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6704Epoch 00199: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6706 - val_loss: 0.0234 - val_acc: 0.6963 Epoch 201/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6645Epoch 00200: val_loss improved from 0.02333 to 0.02309, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6659 - val_loss: 0.0231 - val_acc: 0.6963 Epoch 202/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6498Epoch 00201: val_loss improved from 0.02309 to 0.02297, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6513 - val_loss: 0.0230 - val_acc: 0.6963 Epoch 203/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6745Epoch 00202: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6741 - val_loss: 0.0232 - val_acc: 0.6963 Epoch 204/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6539Epoch 00203: val_loss improved from 0.02297 to 0.02280, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6542 - val_loss: 0.0228 - val_acc: 0.6963 Epoch 205/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6763Epoch 00204: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6764 - val_loss: 0.0230 - val_acc: 0.6963 Epoch 206/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6745Epoch 00205: val_loss improved from 0.02280 to 0.02280, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6752 - val_loss: 0.0228 - val_acc: 0.6963 Epoch 207/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6633Epoch 00206: val_loss improved from 0.02280 to 0.02262, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6630 - val_loss: 0.0226 - val_acc: 0.6963 Epoch 208/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6751Epoch 00207: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6758 - val_loss: 0.0227 - val_acc: 0.6963 Epoch 209/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6745Epoch 00208: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6735 - val_loss: 0.0227 - val_acc: 0.6963 Epoch 210/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6869Epoch 00209: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6875 - val_loss: 0.0227 - val_acc: 0.6963 Epoch 211/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6822Epoch 00210: val_loss improved from 0.02262 to 0.02231, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6799 - val_loss: 0.0223 - val_acc: 0.6963 Epoch 212/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6627Epoch 00211: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6600 - val_loss: 0.0225 - val_acc: 0.6963 Epoch 213/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6639Epoch 00212: val_loss improved from 0.02231 to 0.02227, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6636 - val_loss: 0.0223 - val_acc: 0.6963 Epoch 214/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6698Epoch 00213: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6688 - val_loss: 0.0224 - val_acc: 0.6963 Epoch 215/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6728Epoch 00214: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6735 - val_loss: 0.0224 - val_acc: 0.6963 Epoch 216/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6710Epoch 00215: val_loss improved from 0.02227 to 0.02208, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6729 - val_loss: 0.0221 - val_acc: 0.6963 Epoch 217/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6586Epoch 00216: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6583 - val_loss: 0.0223 - val_acc: 0.6963 Epoch 218/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6592Epoch 00217: val_loss improved from 0.02208 to 0.02190, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6595 - val_loss: 0.0219 - val_acc: 0.6963 Epoch 219/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6663Epoch 00218: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6653 - val_loss: 0.0220 - val_acc: 0.6963 Epoch 220/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6710Epoch 00219: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6706 - val_loss: 0.0219 - val_acc: 0.6963 Epoch 221/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6769Epoch 00220: val_loss improved from 0.02190 to 0.02187, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6764 - val_loss: 0.0219 - val_acc: 0.6963 Epoch 222/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6775Epoch 00221: val_loss improved from 0.02187 to 0.02185, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6764 - val_loss: 0.0219 - val_acc: 0.6963 Epoch 223/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6728Epoch 00222: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6700 - val_loss: 0.0219 - val_acc: 0.6963 Epoch 224/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6633Epoch 00223: val_loss improved from 0.02185 to 0.02179, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6630 - val_loss: 0.0218 - val_acc: 0.6963 Epoch 225/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6787Epoch 00224: val_loss improved from 0.02179 to 0.02176, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6764 - val_loss: 0.0218 - val_acc: 0.6963 Epoch 226/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6627Epoch 00225: val_loss improved from 0.02176 to 0.02155, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6612 - val_loss: 0.0215 - val_acc: 0.6963 Epoch 227/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6733Epoch 00226: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6729 - val_loss: 0.0216 - val_acc: 0.6963 Epoch 228/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6733Epoch 00227: val_loss improved from 0.02155 to 0.02144, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6746 - val_loss: 0.0214 - val_acc: 0.6963 Epoch 229/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6651Epoch 00228: val_loss improved from 0.02144 to 0.02127, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6647 - val_loss: 0.0213 - val_acc: 0.6963 Epoch 230/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6704Epoch 00229: val_loss improved from 0.02127 to 0.02124, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6717 - val_loss: 0.0212 - val_acc: 0.6963 Epoch 231/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6739Epoch 00230: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6746 - val_loss: 0.0214 - val_acc: 0.6963 Epoch 232/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6645Epoch 00231: val_loss improved from 0.02124 to 0.02121, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6653 - val_loss: 0.0212 - val_acc: 0.6963 Epoch 233/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6745Epoch 00232: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6746 - val_loss: 0.0212 - val_acc: 0.6963 Epoch 234/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6792Epoch 00233: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6776 - val_loss: 0.0213 - val_acc: 0.6963 Epoch 235/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6763Epoch 00234: val_loss improved from 0.02121 to 0.02076, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6741 - val_loss: 0.0208 - val_acc: 0.6963 Epoch 236/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6710Epoch 00235: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6711 - val_loss: 0.0210 - val_acc: 0.6963 Epoch 237/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6704Epoch 00236: val_loss did not improve 1712/1712 [==============================] - 2s - loss: 0.0057 - acc: 0.6688 - val_loss: 0.0208 - val_acc: 0.6963 Epoch 238/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6775Epoch 00237: val_loss improved from 0.02076 to 0.02068, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6764 - val_loss: 0.0207 - val_acc: 0.6963 Epoch 239/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6669Epoch 00238: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6671 - val_loss: 0.0209 - val_acc: 0.6963 Epoch 240/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6828Epoch 00239: val_loss improved from 0.02068 to 0.02050, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6811 - val_loss: 0.0205 - val_acc: 0.6963 Epoch 241/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0056 - acc: 0.6686Epoch 00240: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0056 - acc: 0.6706 - val_loss: 0.0206 - val_acc: 0.6963 Epoch 242/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6698Epoch 00241: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6706 - val_loss: 0.0205 - val_acc: 0.6963 Epoch 243/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6798Epoch 00242: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6799 - val_loss: 0.0205 - val_acc: 0.6963 Epoch 244/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6716Epoch 00243: val_loss improved from 0.02050 to 0.02023, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6723 - val_loss: 0.0202 - val_acc: 0.6963 Epoch 245/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0056 - acc: 0.6651Epoch 00244: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0056 - acc: 0.6647 - val_loss: 0.0204 - val_acc: 0.6963 Epoch 246/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0056 - acc: 0.6728Epoch 00245: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0056 - acc: 0.6723 - val_loss: 0.0204 - val_acc: 0.6963 Epoch 247/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6692Epoch 00246: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6694 - val_loss: 0.0204 - val_acc: 0.6963 Epoch 248/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6804Epoch 00247: val_loss improved from 0.02023 to 0.02003, saving model to my_model_SGD.h5 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6787 - val_loss: 0.0200 - val_acc: 0.6963 Epoch 249/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6798Epoch 00248: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6793 - val_loss: 0.0203 - val_acc: 0.6963 Epoch 250/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0056 - acc: 0.6704Epoch 00249: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6717 - val_loss: 0.0201 - val_acc: 0.6963 SGD loss = 0.02009357494589324 Train on 1712 samples, validate on 428 samples Epoch 1/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0334 - acc: 0.5259Epoch 00000: val_loss improved from inf to 0.05367, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0333 - acc: 0.5275 - val_loss: 0.0537 - val_acc: 0.6963 Epoch 2/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.6386Epoch 00001: val_loss improved from 0.05367 to 0.01890, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0104 - acc: 0.6396 - val_loss: 0.0189 - val_acc: 0.6963 Epoch 3/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0071 - acc: 0.6621Epoch 00002: val_loss improved from 0.01890 to 0.00573, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0071 - acc: 0.6630 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 4/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6857Epoch 00003: val_loss improved from 0.00573 to 0.00455, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6869 - val_loss: 0.0045 - val_acc: 0.6963 Epoch 5/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0056 - acc: 0.6940Epoch 00004: val_loss improved from 0.00455 to 0.00447, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0056 - acc: 0.6933 - val_loss: 0.0045 - val_acc: 0.6963 Epoch 6/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.7028Epoch 00005: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0053 - acc: 0.7021 - val_loss: 0.0048 - val_acc: 0.6963 Epoch 7/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.7017Epoch 00006: val_loss improved from 0.00447 to 0.00446, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.7021 - val_loss: 0.0045 - val_acc: 0.6963 Epoch 8/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.6969Epoch 00007: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.6986 - val_loss: 0.0059 - val_acc: 0.6963 Epoch 9/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.6999Epoch 00008: val_loss improved from 0.00446 to 0.00438, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7021 - val_loss: 0.0044 - val_acc: 0.6963 Epoch 10/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7022Epoch 00009: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7021 - val_loss: 0.0044 - val_acc: 0.6963 Epoch 11/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7070Epoch 00010: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7056 - val_loss: 0.0049 - val_acc: 0.6963 Epoch 12/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7040Epoch 00011: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7056 - val_loss: 0.0045 - val_acc: 0.6963 Epoch 13/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7087Epoch 00012: val_loss improved from 0.00438 to 0.00436, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7091 - val_loss: 0.0044 - val_acc: 0.6963 Epoch 14/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7058Epoch 00013: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7044 - val_loss: 0.0045 - val_acc: 0.6963 Epoch 15/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7028Epoch 00014: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7033 - val_loss: 0.0044 - val_acc: 0.6963 Epoch 16/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7087Epoch 00015: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0039 - acc: 0.7074 - val_loss: 0.0046 - val_acc: 0.6963 Epoch 17/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0038 - acc: 0.7087Epoch 00016: val_loss improved from 0.00436 to 0.00418, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0038 - acc: 0.7074 - val_loss: 0.0042 - val_acc: 0.6963 Epoch 18/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0037 - acc: 0.7087Epoch 00017: val_loss improved from 0.00418 to 0.00391, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0037 - acc: 0.7074 - val_loss: 0.0039 - val_acc: 0.6939 Epoch 19/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0035 - acc: 0.7064Epoch 00018: val_loss improved from 0.00391 to 0.00364, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0035 - acc: 0.7079 - val_loss: 0.0036 - val_acc: 0.6963 Epoch 20/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0034 - acc: 0.6969Epoch 00019: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0034 - acc: 0.6974 - val_loss: 0.0037 - val_acc: 0.6963 Epoch 21/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0032 - acc: 0.7034Epoch 00020: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0032 - acc: 0.7044 - val_loss: 0.0037 - val_acc: 0.6986 Epoch 22/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0031 - acc: 0.7252Epoch 00021: val_loss improved from 0.00364 to 0.00325, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0031 - acc: 0.7261 - val_loss: 0.0032 - val_acc: 0.6963 Epoch 23/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0030 - acc: 0.7276Epoch 00022: val_loss improved from 0.00325 to 0.00286, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0030 - acc: 0.7266 - val_loss: 0.0029 - val_acc: 0.7196 Epoch 24/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0029 - acc: 0.7229Epoch 00023: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0029 - acc: 0.7237 - val_loss: 0.0032 - val_acc: 0.6963 Epoch 25/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0028 - acc: 0.7217Epoch 00024: val_loss improved from 0.00286 to 0.00275, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0028 - acc: 0.7214 - val_loss: 0.0028 - val_acc: 0.7033 Epoch 26/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0026 - acc: 0.7264Epoch 00025: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0026 - acc: 0.7261 - val_loss: 0.0033 - val_acc: 0.7126 Epoch 27/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0025 - acc: 0.7223Epoch 00026: val_loss improved from 0.00275 to 0.00274, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0025 - acc: 0.7225 - val_loss: 0.0027 - val_acc: 0.7009 Epoch 28/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0025 - acc: 0.7317Epoch 00027: val_loss improved from 0.00274 to 0.00250, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0025 - acc: 0.7313 - val_loss: 0.0025 - val_acc: 0.7103 Epoch 29/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0024 - acc: 0.7364Epoch 00028: val_loss improved from 0.00250 to 0.00229, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0024 - acc: 0.7360 - val_loss: 0.0023 - val_acc: 0.7290 Epoch 30/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0024 - acc: 0.7341Epoch 00029: val_loss improved from 0.00229 to 0.00219, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0024 - acc: 0.7348 - val_loss: 0.0022 - val_acc: 0.7336 Epoch 31/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0023 - acc: 0.7382Epoch 00030: val_loss improved from 0.00219 to 0.00217, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0023 - acc: 0.7377 - val_loss: 0.0022 - val_acc: 0.7220 Epoch 32/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0023 - acc: 0.7429Epoch 00031: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0023 - acc: 0.7430 - val_loss: 0.0023 - val_acc: 0.7196 Epoch 33/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0021 - acc: 0.7252Epoch 00032: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0021 - acc: 0.7261 - val_loss: 0.0022 - val_acc: 0.7407 Epoch 34/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0021 - acc: 0.7364Epoch 00033: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0021 - acc: 0.7377 - val_loss: 0.0025 - val_acc: 0.7103 Epoch 35/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0021 - acc: 0.7529Epoch 00034: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0021 - acc: 0.7512 - val_loss: 0.0023 - val_acc: 0.7383 Epoch 36/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0020 - acc: 0.7353Epoch 00035: val_loss improved from 0.00217 to 0.00196, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0020 - acc: 0.7354 - val_loss: 0.0020 - val_acc: 0.7196 Epoch 37/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0020 - acc: 0.7429Epoch 00036: val_loss improved from 0.00196 to 0.00183, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0020 - acc: 0.7442 - val_loss: 0.0018 - val_acc: 0.7360 Epoch 38/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0019 - acc: 0.7476Epoch 00037: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0019 - acc: 0.7477 - val_loss: 0.0020 - val_acc: 0.7336 Epoch 39/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0019 - acc: 0.7518Epoch 00038: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0019 - acc: 0.7512 - val_loss: 0.0022 - val_acc: 0.7079 Epoch 40/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0019 - acc: 0.7529Epoch 00039: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0019 - acc: 0.7541 - val_loss: 0.0023 - val_acc: 0.7477 Epoch 41/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0018 - acc: 0.7512Epoch 00040: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0018 - acc: 0.7512 - val_loss: 0.0019 - val_acc: 0.7453 Epoch 42/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0018 - acc: 0.7706Epoch 00041: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0018 - acc: 0.7716 - val_loss: 0.0021 - val_acc: 0.7290 Epoch 43/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0017 - acc: 0.7541Epoch 00042: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0017 - acc: 0.7558 - val_loss: 0.0021 - val_acc: 0.7266 Epoch 44/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0017 - acc: 0.7700Epoch 00043: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0017 - acc: 0.7687 - val_loss: 0.0020 - val_acc: 0.7500 Epoch 45/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0017 - acc: 0.7624Epoch 00044: val_loss improved from 0.00183 to 0.00170, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0017 - acc: 0.7640 - val_loss: 0.0017 - val_acc: 0.7383 Epoch 46/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0017 - acc: 0.7600Epoch 00045: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0017 - acc: 0.7593 - val_loss: 0.0020 - val_acc: 0.7173 Epoch 47/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0016 - acc: 0.7718Epoch 00046: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0016 - acc: 0.7710 - val_loss: 0.0020 - val_acc: 0.7547 Epoch 48/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0017 - acc: 0.7642Epoch 00047: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0017 - acc: 0.7652 - val_loss: 0.0024 - val_acc: 0.7430 Epoch 49/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0016 - acc: 0.7606Epoch 00048: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0016 - acc: 0.7611 - val_loss: 0.0018 - val_acc: 0.7196 Epoch 50/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0016 - acc: 0.7718Epoch 00049: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0016 - acc: 0.7716 - val_loss: 0.0020 - val_acc: 0.7407 Epoch 51/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0015 - acc: 0.7636Epoch 00050: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0015 - acc: 0.7646 - val_loss: 0.0021 - val_acc: 0.7220 Epoch 52/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0015 - acc: 0.7630Epoch 00051: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0015 - acc: 0.7629 - val_loss: 0.0022 - val_acc: 0.7290 Epoch 53/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0016 - acc: 0.7736Epoch 00052: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0016 - acc: 0.7734 - val_loss: 0.0018 - val_acc: 0.7617 Epoch 54/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7771Epoch 00053: val_loss improved from 0.00170 to 0.00164, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7780 - val_loss: 0.0016 - val_acc: 0.7827 Epoch 55/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0015 - acc: 0.7718Epoch 00054: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0015 - acc: 0.7716 - val_loss: 0.0018 - val_acc: 0.7453 Epoch 56/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7830Epoch 00055: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7850 - val_loss: 0.0019 - val_acc: 0.7640 Epoch 57/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0015 - acc: 0.7842Epoch 00056: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0015 - acc: 0.7850 - val_loss: 0.0017 - val_acc: 0.7570 Epoch 58/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7700Epoch 00057: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7704 - val_loss: 0.0017 - val_acc: 0.7290 Epoch 59/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7854Epoch 00058: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7856 - val_loss: 0.0020 - val_acc: 0.7710 Epoch 60/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7842Epoch 00059: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7845 - val_loss: 0.0017 - val_acc: 0.7407 Epoch 61/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7795Epoch 00060: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7792 - val_loss: 0.0021 - val_acc: 0.7804 Epoch 62/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7812Epoch 00061: val_loss improved from 0.00164 to 0.00164, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7827 - val_loss: 0.0016 - val_acc: 0.7266 Epoch 63/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7848Epoch 00062: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7856 - val_loss: 0.0017 - val_acc: 0.7593 Epoch 64/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7801Epoch 00063: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7798 - val_loss: 0.0019 - val_acc: 0.7780 Epoch 65/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7754Epoch 00064: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7763 - val_loss: 0.0016 - val_acc: 0.7897 Epoch 66/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7936Epoch 00065: val_loss improved from 0.00164 to 0.00157, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7944 - val_loss: 0.0016 - val_acc: 0.7664 Epoch 67/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7919Epoch 00066: val_loss improved from 0.00157 to 0.00153, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7921 - val_loss: 0.0015 - val_acc: 0.7500 Epoch 68/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7860Epoch 00067: val_loss improved from 0.00153 to 0.00151, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7862 - val_loss: 0.0015 - val_acc: 0.7710 Epoch 69/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7742Epoch 00068: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7734 - val_loss: 0.0017 - val_acc: 0.7664 Epoch 70/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7930Epoch 00069: val_loss improved from 0.00151 to 0.00150, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7909 - val_loss: 0.0015 - val_acc: 0.7804 Epoch 71/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7871Epoch 00070: val_loss improved from 0.00150 to 0.00138, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7880 - val_loss: 0.0014 - val_acc: 0.7547 Epoch 72/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7954Epoch 00071: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7973 - val_loss: 0.0018 - val_acc: 0.7290 Epoch 73/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7913Epoch 00072: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7926 - val_loss: 0.0016 - val_acc: 0.7640 Epoch 74/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7901Epoch 00073: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7880 - val_loss: 0.0015 - val_acc: 0.7336 Epoch 75/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7948Epoch 00074: val_loss improved from 0.00138 to 0.00137, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7967 - val_loss: 0.0014 - val_acc: 0.7687 Epoch 76/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.8078Epoch 00075: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.8067 - val_loss: 0.0015 - val_acc: 0.7664 Epoch 77/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.8054Epoch 00076: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.8067 - val_loss: 0.0015 - val_acc: 0.7640 Epoch 78/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7907Epoch 00077: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7909 - val_loss: 0.0017 - val_acc: 0.7523 Epoch 79/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7954Epoch 00078: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7956 - val_loss: 0.0014 - val_acc: 0.7780 Epoch 80/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.8037Epoch 00079: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.8043 - val_loss: 0.0016 - val_acc: 0.7897 Epoch 81/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7907Epoch 00080: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7921 - val_loss: 0.0021 - val_acc: 0.7593 Epoch 82/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.8125Epoch 00081: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.8131 - val_loss: 0.0018 - val_acc: 0.7827 Epoch 83/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7907Epoch 00082: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7915 - val_loss: 0.0015 - val_acc: 0.7617 Epoch 84/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.8025Epoch 00083: val_loss improved from 0.00137 to 0.00123, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.8014 - val_loss: 0.0012 - val_acc: 0.7734 Epoch 85/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.8072Epoch 00084: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.8078 - val_loss: 0.0017 - val_acc: 0.7617 Epoch 86/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.8066Epoch 00085: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.8067 - val_loss: 0.0016 - val_acc: 0.7523 Epoch 87/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7989Epoch 00086: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7991 - val_loss: 0.0014 - val_acc: 0.7523 Epoch 88/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.8019Epoch 00087: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.8002 - val_loss: 0.0014 - val_acc: 0.7523 Epoch 89/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.8143Epoch 00088: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.8137 - val_loss: 0.0013 - val_acc: 0.8014 Epoch 90/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.8031Epoch 00089: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.8032 - val_loss: 0.0015 - val_acc: 0.7687 Epoch 91/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.8037Epoch 00090: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.8032 - val_loss: 0.0014 - val_acc: 0.7547 Epoch 92/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.7978Epoch 00091: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.7985 - val_loss: 0.0014 - val_acc: 0.7780 Epoch 93/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.8202Epoch 00092: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.8189 - val_loss: 0.0015 - val_acc: 0.7897 Epoch 94/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.8013Epoch 00093: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.8026 - val_loss: 0.0016 - val_acc: 0.7827 Epoch 95/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.8184Epoch 00094: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.8183 - val_loss: 0.0017 - val_acc: 0.7570 Epoch 96/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.8328e-04 - acc: 0.7995Epoch 00095: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.8007e-04 - acc: 0.8008 - val_loss: 0.0014 - val_acc: 0.7593 Epoch 97/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.8255Epoch 00096: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.8259 - val_loss: 0.0017 - val_acc: 0.7687 Epoch 98/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.8137Epoch 00097: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.8148 - val_loss: 0.0014 - val_acc: 0.7617 Epoch 99/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.8022e-04 - acc: 0.8166Epoch 00098: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.7933e-04 - acc: 0.8166 - val_loss: 0.0013 - val_acc: 0.7874 Epoch 100/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.7487e-04 - acc: 0.8160Epoch 00099: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.7455e-04 - acc: 0.8160 - val_loss: 0.0015 - val_acc: 0.7430 Epoch 101/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.8131 Epoch 00100: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.8125 - val_loss: 0.0019 - val_acc: 0.7570 Epoch 102/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.6895e-04 - acc: 0.7989Epoch 00101: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.6767e-04 - acc: 0.8008 - val_loss: 0.0013 - val_acc: 0.7757 Epoch 103/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.7031e-04 - acc: 0.8113Epoch 00102: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.6782e-04 - acc: 0.8119 - val_loss: 0.0014 - val_acc: 0.7523 Epoch 104/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.5976e-04 - acc: 0.8125Epoch 00103: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.5915e-04 - acc: 0.8119 - val_loss: 0.0013 - val_acc: 0.7804 Epoch 105/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.5293e-04 - acc: 0.8219Epoch 00104: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.5003e-04 - acc: 0.8218 - val_loss: 0.0013 - val_acc: 0.7850 Epoch 106/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.4862e-04 - acc: 0.8190Epoch 00105: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.5298e-04 - acc: 0.8195 - val_loss: 0.0014 - val_acc: 0.7617 Epoch 107/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.5401e-04 - acc: 0.8137Epoch 00106: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.5647e-04 - acc: 0.8131 - val_loss: 0.0014 - val_acc: 0.7734 Epoch 108/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.2870e-04 - acc: 0.8084Epoch 00107: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.2594e-04 - acc: 0.8090 - val_loss: 0.0013 - val_acc: 0.7664 Epoch 109/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.2772e-04 - acc: 0.8037Epoch 00108: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.2547e-04 - acc: 0.8032 - val_loss: 0.0013 - val_acc: 0.7827 Epoch 110/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.2787e-04 - acc: 0.8054Epoch 00109: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.2595e-04 - acc: 0.8055 - val_loss: 0.0013 - val_acc: 0.7921 Epoch 111/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.4517e-04 - acc: 0.8284Epoch 00110: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.4473e-04 - acc: 0.8294 - val_loss: 0.0014 - val_acc: 0.7991 Epoch 112/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.1939e-04 - acc: 0.8231Epoch 00111: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.1944e-04 - acc: 0.8236 - val_loss: 0.0014 - val_acc: 0.7570 Epoch 113/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.4602e-04 - acc: 0.8137Epoch 00112: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.4523e-04 - acc: 0.8143 - val_loss: 0.0016 - val_acc: 0.7617 Epoch 114/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.9892e-04 - acc: 0.8219Epoch 00113: val_loss improved from 0.00123 to 0.00122, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 8.9672e-04 - acc: 0.8218 - val_loss: 0.0012 - val_acc: 0.7734 Epoch 115/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.0593e-04 - acc: 0.8143Epoch 00114: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.0510e-04 - acc: 0.8143 - val_loss: 0.0014 - val_acc: 0.7780 Epoch 116/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.0569e-04 - acc: 0.8331Epoch 00115: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.0416e-04 - acc: 0.8341 - val_loss: 0.0016 - val_acc: 0.7804 Epoch 117/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.1526e-04 - acc: 0.8196Epoch 00116: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.1264e-04 - acc: 0.8195 - val_loss: 0.0013 - val_acc: 0.7827 Epoch 118/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.7680e-04 - acc: 0.8143Epoch 00117: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.7995e-04 - acc: 0.8143 - val_loss: 0.0013 - val_acc: 0.7897 Epoch 119/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.8741e-04 - acc: 0.8243Epoch 00118: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.8620e-04 - acc: 0.8248 - val_loss: 0.0013 - val_acc: 0.8037 Epoch 120/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.7087e-04 - acc: 0.8154Epoch 00119: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.7051e-04 - acc: 0.8166 - val_loss: 0.0013 - val_acc: 0.7687 Epoch 121/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.0259e-04 - acc: 0.8196Epoch 00120: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.0166e-04 - acc: 0.8201 - val_loss: 0.0014 - val_acc: 0.7804 Epoch 122/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.5187e-04 - acc: 0.8160Epoch 00121: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.5199e-04 - acc: 0.8172 - val_loss: 0.0013 - val_acc: 0.7850 Epoch 123/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.8945e-04 - acc: 0.8290Epoch 00122: val_loss improved from 0.00122 to 0.00118, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 8.9136e-04 - acc: 0.8300 - val_loss: 0.0012 - val_acc: 0.7897 Epoch 124/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.5430e-04 - acc: 0.8219Epoch 00123: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.5971e-04 - acc: 0.8207 - val_loss: 0.0015 - val_acc: 0.8037 Epoch 125/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.5033e-04 - acc: 0.8272Epoch 00124: val_loss improved from 0.00118 to 0.00114, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 8.4929e-04 - acc: 0.8289 - val_loss: 0.0011 - val_acc: 0.8037 Epoch 126/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.5249e-04 - acc: 0.8219Epoch 00125: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.5062e-04 - acc: 0.8230 - val_loss: 0.0013 - val_acc: 0.7757 Epoch 127/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.3736e-04 - acc: 0.8243Epoch 00126: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.3755e-04 - acc: 0.8242 - val_loss: 0.0013 - val_acc: 0.7944 Epoch 128/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.4999e-04 - acc: 0.8149Epoch 00127: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.5012e-04 - acc: 0.8166 - val_loss: 0.0015 - val_acc: 0.7921 Epoch 129/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.4238e-04 - acc: 0.8249Epoch 00128: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.4266e-04 - acc: 0.8254 - val_loss: 0.0015 - val_acc: 0.7780 Epoch 130/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.2898e-04 - acc: 0.8272Epoch 00129: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.2868e-04 - acc: 0.8259 - val_loss: 0.0012 - val_acc: 0.8014 Epoch 131/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.2124e-04 - acc: 0.8255Epoch 00130: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.2044e-04 - acc: 0.8242 - val_loss: 0.0015 - val_acc: 0.7780 Epoch 132/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.0530e-04 - acc: 0.8308Epoch 00131: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.0904e-04 - acc: 0.8306 - val_loss: 0.0014 - val_acc: 0.8154 Epoch 133/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.4800e-04 - acc: 0.8290Epoch 00132: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.4766e-04 - acc: 0.8289 - val_loss: 0.0014 - val_acc: 0.8061 Epoch 134/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.2912e-04 - acc: 0.8219Epoch 00133: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.2796e-04 - acc: 0.8236 - val_loss: 0.0012 - val_acc: 0.7991 Epoch 135/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.1735e-04 - acc: 0.8355Epoch 00134: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.1697e-04 - acc: 0.8353 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 136/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.2776e-04 - acc: 0.8213Epoch 00135: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.3062e-04 - acc: 0.8201 - val_loss: 0.0012 - val_acc: 0.7804 Epoch 137/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.9138e-04 - acc: 0.8355Epoch 00136: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.9188e-04 - acc: 0.8347 - val_loss: 0.0013 - val_acc: 0.8084 Epoch 138/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.0166e-04 - acc: 0.8349Epoch 00137: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.0184e-04 - acc: 0.8353 - val_loss: 0.0014 - val_acc: 0.7593 Epoch 139/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.1827e-04 - acc: 0.8261Epoch 00138: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.1595e-04 - acc: 0.8271 - val_loss: 0.0013 - val_acc: 0.7944 Epoch 140/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.0259e-04 - acc: 0.8290Epoch 00139: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.0046e-04 - acc: 0.8294 - val_loss: 0.0014 - val_acc: 0.7734 Epoch 141/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.7814e-04 - acc: 0.8178Epoch 00140: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.7598e-04 - acc: 0.8189 - val_loss: 0.0012 - val_acc: 0.7640 Epoch 142/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.0314e-04 - acc: 0.8219Epoch 00141: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.0305e-04 - acc: 0.8224 - val_loss: 0.0013 - val_acc: 0.7967 Epoch 143/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.9212e-04 - acc: 0.8261Epoch 00142: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.9269e-04 - acc: 0.8265 - val_loss: 0.0013 - val_acc: 0.7921 Epoch 144/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.7152e-04 - acc: 0.8479Epoch 00143: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.7322e-04 - acc: 0.8481 - val_loss: 0.0012 - val_acc: 0.7804 Epoch 145/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.9985e-04 - acc: 0.8267Epoch 00144: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.0045e-04 - acc: 0.8259 - val_loss: 0.0012 - val_acc: 0.8178 Epoch 146/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6715e-04 - acc: 0.8213Epoch 00145: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6471e-04 - acc: 0.8224 - val_loss: 0.0012 - val_acc: 0.8037 Epoch 147/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.8460e-04 - acc: 0.8379Epoch 00146: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.8496e-04 - acc: 0.8382 - val_loss: 0.0013 - val_acc: 0.7710 Epoch 148/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.7272e-04 - acc: 0.8296Epoch 00147: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.7522e-04 - acc: 0.8300 - val_loss: 0.0012 - val_acc: 0.7710 Epoch 149/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5536e-04 - acc: 0.8443Epoch 00148: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5391e-04 - acc: 0.8452 - val_loss: 0.0011 - val_acc: 0.7991 Epoch 150/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5552e-04 - acc: 0.8314Epoch 00149: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5625e-04 - acc: 0.8318 - val_loss: 0.0012 - val_acc: 0.8061 Epoch 151/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6997e-04 - acc: 0.8308Epoch 00150: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.7250e-04 - acc: 0.8289 - val_loss: 0.0012 - val_acc: 0.7593 Epoch 152/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6537e-04 - acc: 0.8461Epoch 00151: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6435e-04 - acc: 0.8458 - val_loss: 0.0012 - val_acc: 0.8037 Epoch 153/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3463e-04 - acc: 0.8438Epoch 00152: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.3315e-04 - acc: 0.8440 - val_loss: 0.0013 - val_acc: 0.7804 Epoch 154/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5992e-04 - acc: 0.8243Epoch 00153: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6387e-04 - acc: 0.8236 - val_loss: 0.0012 - val_acc: 0.7687 Epoch 155/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4819e-04 - acc: 0.8337Epoch 00154: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4580e-04 - acc: 0.8347 - val_loss: 0.0014 - val_acc: 0.7874 Epoch 156/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4349e-04 - acc: 0.8272Epoch 00155: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4585e-04 - acc: 0.8259 - val_loss: 0.0013 - val_acc: 0.7804 Epoch 157/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5107e-04 - acc: 0.8314Epoch 00156: val_loss improved from 0.00114 to 0.00105, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 7.5002e-04 - acc: 0.8318 - val_loss: 0.0011 - val_acc: 0.8061 Epoch 158/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3134e-04 - acc: 0.8231Epoch 00157: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.3163e-04 - acc: 0.8230 - val_loss: 0.0014 - val_acc: 0.7874 Epoch 159/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3394e-04 - acc: 0.8343Epoch 00158: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.3486e-04 - acc: 0.8341 - val_loss: 0.0013 - val_acc: 0.7897 Epoch 160/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5859e-04 - acc: 0.8373Epoch 00159: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5894e-04 - acc: 0.8376 - val_loss: 0.0015 - val_acc: 0.7734 Epoch 161/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3431e-04 - acc: 0.8308Epoch 00160: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.3235e-04 - acc: 0.8306 - val_loss: 0.0013 - val_acc: 0.8061 Epoch 162/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1876e-04 - acc: 0.8278Epoch 00161: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.1848e-04 - acc: 0.8277 - val_loss: 0.0014 - val_acc: 0.7850 Epoch 163/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.2588e-04 - acc: 0.8361Epoch 00162: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.2629e-04 - acc: 0.8370 - val_loss: 0.0014 - val_acc: 0.7593 Epoch 164/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1786e-04 - acc: 0.8325Epoch 00163: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.1765e-04 - acc: 0.8335 - val_loss: 0.0012 - val_acc: 0.7850 Epoch 165/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3719e-04 - acc: 0.8308Epoch 00164: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.3628e-04 - acc: 0.8312 - val_loss: 0.0011 - val_acc: 0.8014 Epoch 166/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.0280e-04 - acc: 0.8255Epoch 00165: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.0161e-04 - acc: 0.8259 - val_loss: 0.0013 - val_acc: 0.7850 Epoch 167/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1776e-04 - acc: 0.8261Epoch 00166: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.1847e-04 - acc: 0.8265 - val_loss: 0.0012 - val_acc: 0.7874 Epoch 168/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.0498e-04 - acc: 0.8349Epoch 00167: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.0566e-04 - acc: 0.8353 - val_loss: 0.0011 - val_acc: 0.7804 Epoch 169/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.0932e-04 - acc: 0.8367Epoch 00168: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.0808e-04 - acc: 0.8370 - val_loss: 0.0012 - val_acc: 0.7850 Epoch 170/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.0646e-04 - acc: 0.8325Epoch 00169: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.0627e-04 - acc: 0.8341 - val_loss: 0.0012 - val_acc: 0.7804 Epoch 171/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.9548e-04 - acc: 0.8455Epoch 00170: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.9554e-04 - acc: 0.8446 - val_loss: 0.0014 - val_acc: 0.7991 Epoch 172/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1062e-04 - acc: 0.8302Epoch 00171: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.0894e-04 - acc: 0.8300 - val_loss: 0.0013 - val_acc: 0.7921 Epoch 173/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.0694e-04 - acc: 0.8302Epoch 00172: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.0845e-04 - acc: 0.8300 - val_loss: 0.0012 - val_acc: 0.7897 Epoch 174/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.9922e-04 - acc: 0.8172Epoch 00173: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.9959e-04 - acc: 0.8183 - val_loss: 0.0012 - val_acc: 0.7757 Epoch 175/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.7221e-04 - acc: 0.8331Epoch 00174: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.7094e-04 - acc: 0.8335 - val_loss: 0.0013 - val_acc: 0.7897 Epoch 176/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.9216e-04 - acc: 0.8272Epoch 00175: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.9307e-04 - acc: 0.8283 - val_loss: 0.0013 - val_acc: 0.7991 Epoch 177/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.9240e-04 - acc: 0.8331Epoch 00176: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.9180e-04 - acc: 0.8318 - val_loss: 0.0011 - val_acc: 0.7827 Epoch 178/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.6767e-04 - acc: 0.8314Epoch 00177: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.6954e-04 - acc: 0.8294 - val_loss: 0.0013 - val_acc: 0.7757 Epoch 179/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.7187e-04 - acc: 0.8284Epoch 00178: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.7149e-04 - acc: 0.8277 - val_loss: 0.0014 - val_acc: 0.7687 Epoch 180/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.6706e-04 - acc: 0.8443Epoch 00179: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.6966e-04 - acc: 0.8435 - val_loss: 0.0012 - val_acc: 0.7921 Epoch 181/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.6065e-04 - acc: 0.8384Epoch 00180: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.6192e-04 - acc: 0.8382 - val_loss: 0.0011 - val_acc: 0.7944 Epoch 182/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.6797e-04 - acc: 0.8314Epoch 00181: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.6732e-04 - acc: 0.8324 - val_loss: 0.0012 - val_acc: 0.7921 Epoch 183/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.8155e-04 - acc: 0.8361Epoch 00182: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.8412e-04 - acc: 0.8370 - val_loss: 0.0013 - val_acc: 0.8037 Epoch 184/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.7380e-04 - acc: 0.8455Epoch 00183: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.7302e-04 - acc: 0.8446 - val_loss: 0.0012 - val_acc: 0.7874 Epoch 185/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.5439e-04 - acc: 0.8231Epoch 00184: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.5519e-04 - acc: 0.8230 - val_loss: 0.0013 - val_acc: 0.7780 Epoch 186/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.7252e-04 - acc: 0.8432Epoch 00185: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.7266e-04 - acc: 0.8435 - val_loss: 0.0014 - val_acc: 0.7780 Epoch 187/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.7333e-04 - acc: 0.8420Epoch 00186: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.7392e-04 - acc: 0.8435 - val_loss: 0.0012 - val_acc: 0.7710 Epoch 188/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.6929e-04 - acc: 0.8408Epoch 00187: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.7068e-04 - acc: 0.8405 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 189/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.6017e-04 - acc: 0.8379Epoch 00188: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.6129e-04 - acc: 0.8388 - val_loss: 0.0013 - val_acc: 0.8084 Epoch 190/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.4333e-04 - acc: 0.8390Epoch 00189: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.4274e-04 - acc: 0.8382 - val_loss: 0.0011 - val_acc: 0.7991 Epoch 191/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.6823e-04 - acc: 0.8479Epoch 00190: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.6809e-04 - acc: 0.8470 - val_loss: 0.0013 - val_acc: 0.7991 Epoch 192/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.6079e-04 - acc: 0.8284Epoch 00191: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.5910e-04 - acc: 0.8294 - val_loss: 0.0011 - val_acc: 0.7850 Epoch 193/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.4826e-04 - acc: 0.8290Epoch 00192: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.4858e-04 - acc: 0.8294 - val_loss: 0.0012 - val_acc: 0.7734 Epoch 194/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.4395e-04 - acc: 0.8355Epoch 00193: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.4277e-04 - acc: 0.8364 - val_loss: 0.0012 - val_acc: 0.7850 Epoch 195/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.4202e-04 - acc: 0.8361Epoch 00194: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.4126e-04 - acc: 0.8364 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 196/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.5046e-04 - acc: 0.8396Epoch 00195: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.5019e-04 - acc: 0.8394 - val_loss: 0.0012 - val_acc: 0.7827 Epoch 197/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.6314e-04 - acc: 0.8408Epoch 00196: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.6324e-04 - acc: 0.8411 - val_loss: 0.0013 - val_acc: 0.8014 Epoch 198/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.4939e-04 - acc: 0.8473Epoch 00197: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.4807e-04 - acc: 0.8481 - val_loss: 0.0012 - val_acc: 0.7593 Epoch 199/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.5393e-04 - acc: 0.8432Epoch 00198: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.5345e-04 - acc: 0.8435 - val_loss: 0.0012 - val_acc: 0.8014 Epoch 200/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.5031e-04 - acc: 0.8461Epoch 00199: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.4900e-04 - acc: 0.8470 - val_loss: 0.0011 - val_acc: 0.7804 Epoch 201/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3277e-04 - acc: 0.8390Epoch 00200: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3317e-04 - acc: 0.8388 - val_loss: 0.0012 - val_acc: 0.7944 Epoch 202/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.4756e-04 - acc: 0.8367Epoch 00201: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.4644e-04 - acc: 0.8359 - val_loss: 0.0012 - val_acc: 0.7874 Epoch 203/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3553e-04 - acc: 0.8538Epoch 00202: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3710e-04 - acc: 0.8540 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 204/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.2072e-04 - acc: 0.8449Epoch 00203: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.2055e-04 - acc: 0.8458 - val_loss: 0.0012 - val_acc: 0.7850 Epoch 205/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.5272e-04 - acc: 0.8343Epoch 00204: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.5138e-04 - acc: 0.8347 - val_loss: 0.0011 - val_acc: 0.7897 Epoch 206/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.4943e-04 - acc: 0.8367Epoch 00205: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.4816e-04 - acc: 0.8370 - val_loss: 0.0011 - val_acc: 0.7780 Epoch 207/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3744e-04 - acc: 0.8449Epoch 00206: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3904e-04 - acc: 0.8458 - val_loss: 0.0012 - val_acc: 0.7827 Epoch 208/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.2924e-04 - acc: 0.8443Epoch 00207: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.2973e-04 - acc: 0.8435 - val_loss: 0.0012 - val_acc: 0.7757 Epoch 209/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.2046e-04 - acc: 0.8361Epoch 00208: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.2058e-04 - acc: 0.8370 - val_loss: 0.0012 - val_acc: 0.7687 Epoch 210/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.2131e-04 - acc: 0.8367Epoch 00209: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.2277e-04 - acc: 0.8376 - val_loss: 0.0012 - val_acc: 0.7897 Epoch 211/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.2714e-04 - acc: 0.8449Epoch 00210: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.2732e-04 - acc: 0.8446 - val_loss: 0.0013 - val_acc: 0.7944 Epoch 212/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3095e-04 - acc: 0.8426Epoch 00211: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3186e-04 - acc: 0.8423 - val_loss: 0.0012 - val_acc: 0.7944 Epoch 213/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.4142e-04 - acc: 0.8443Epoch 00212: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.4084e-04 - acc: 0.8452 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 214/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.2327e-04 - acc: 0.8384Epoch 00213: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.2157e-04 - acc: 0.8388 - val_loss: 0.0012 - val_acc: 0.7921 Epoch 215/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1475e-04 - acc: 0.8502Epoch 00214: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1431e-04 - acc: 0.8499 - val_loss: 0.0012 - val_acc: 0.8037 Epoch 216/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.0964e-04 - acc: 0.8591Epoch 00215: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.0876e-04 - acc: 0.8604 - val_loss: 0.0011 - val_acc: 0.7921 Epoch 217/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3046e-04 - acc: 0.8502Epoch 00216: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3090e-04 - acc: 0.8505 - val_loss: 0.0011 - val_acc: 0.7757 Epoch 218/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1688e-04 - acc: 0.8473Epoch 00217: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1639e-04 - acc: 0.8475 - val_loss: 0.0013 - val_acc: 0.7710 Epoch 219/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.2618e-04 - acc: 0.8485Epoch 00218: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.2645e-04 - acc: 0.8481 - val_loss: 0.0011 - val_acc: 0.7897 Epoch 220/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1107e-04 - acc: 0.8302Epoch 00219: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1246e-04 - acc: 0.8312 - val_loss: 0.0011 - val_acc: 0.7640 Epoch 221/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.0928e-04 - acc: 0.8367Epoch 00220: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.0859e-04 - acc: 0.8364 - val_loss: 0.0012 - val_acc: 0.8084 Epoch 222/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.0688e-04 - acc: 0.8514Epoch 00221: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.0613e-04 - acc: 0.8505 - val_loss: 0.0011 - val_acc: 0.7780 Epoch 223/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.0987e-04 - acc: 0.8349Epoch 00222: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1302e-04 - acc: 0.8347 - val_loss: 0.0014 - val_acc: 0.7804 Epoch 224/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1432e-04 - acc: 0.8479Epoch 00223: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1259e-04 - acc: 0.8481 - val_loss: 0.0011 - val_acc: 0.7687 Epoch 225/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1123e-04 - acc: 0.8384Epoch 00224: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1083e-04 - acc: 0.8382 - val_loss: 0.0013 - val_acc: 0.7897 Epoch 226/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1112e-04 - acc: 0.8384Epoch 00225: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1220e-04 - acc: 0.8382 - val_loss: 0.0012 - val_acc: 0.7617 Epoch 227/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.2537e-04 - acc: 0.8438Epoch 00226: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.2619e-04 - acc: 0.8435 - val_loss: 0.0011 - val_acc: 0.7944 Epoch 228/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.0300e-04 - acc: 0.8555Epoch 00227: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.0335e-04 - acc: 0.8551 - val_loss: 0.0011 - val_acc: 0.7710 Epoch 229/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9679e-04 - acc: 0.8390Epoch 00228: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9676e-04 - acc: 0.8388 - val_loss: 0.0011 - val_acc: 0.7757 Epoch 230/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1403e-04 - acc: 0.8331Epoch 00229: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1490e-04 - acc: 0.8341 - val_loss: 0.0012 - val_acc: 0.7897 Epoch 231/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.0585e-04 - acc: 0.8414Epoch 00230: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.0390e-04 - acc: 0.8423 - val_loss: 0.0011 - val_acc: 0.7944 Epoch 232/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1846e-04 - acc: 0.8373Epoch 00231: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1872e-04 - acc: 0.8382 - val_loss: 0.0011 - val_acc: 0.7710 Epoch 233/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.0299e-04 - acc: 0.8414Epoch 00232: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.0223e-04 - acc: 0.8388 - val_loss: 0.0011 - val_acc: 0.7874 Epoch 234/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9893e-04 - acc: 0.8461Epoch 00233: val_loss improved from 0.00105 to 0.00100, saving model to my_model_RMSprop.h5 1712/1712 [==============================] - 3s - loss: 5.9844e-04 - acc: 0.8475 - val_loss: 9.9615e-04 - val_acc: 0.7827 Epoch 235/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1583e-04 - acc: 0.8455Epoch 00234: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1616e-04 - acc: 0.8464 - val_loss: 0.0011 - val_acc: 0.8014 Epoch 236/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.0027e-04 - acc: 0.8514Epoch 00235: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.0134e-04 - acc: 0.8505 - val_loss: 0.0012 - val_acc: 0.7944 Epoch 237/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.0206e-04 - acc: 0.8408Epoch 00236: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.0265e-04 - acc: 0.8394 - val_loss: 0.0011 - val_acc: 0.8131 Epoch 238/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8454e-04 - acc: 0.8520Epoch 00237: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8378e-04 - acc: 0.8528 - val_loss: 0.0011 - val_acc: 0.7991 Epoch 239/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9630e-04 - acc: 0.8479Epoch 00238: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9555e-04 - acc: 0.8481 - val_loss: 0.0013 - val_acc: 0.7780 Epoch 240/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8981e-04 - acc: 0.8508Epoch 00239: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9057e-04 - acc: 0.8516 - val_loss: 0.0011 - val_acc: 0.7991 Epoch 241/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9829e-04 - acc: 0.8426Epoch 00240: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9646e-04 - acc: 0.8440 - val_loss: 0.0011 - val_acc: 0.7827 Epoch 242/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1232e-04 - acc: 0.8449Epoch 00241: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1157e-04 - acc: 0.8446 - val_loss: 0.0010 - val_acc: 0.8084 Epoch 243/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9673e-04 - acc: 0.8473Epoch 00242: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9714e-04 - acc: 0.8470 - val_loss: 0.0011 - val_acc: 0.7780 Epoch 244/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.0372e-04 - acc: 0.8491Epoch 00243: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.0377e-04 - acc: 0.8481 - val_loss: 0.0010 - val_acc: 0.7874 Epoch 245/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9918e-04 - acc: 0.8520Epoch 00244: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9967e-04 - acc: 0.8522 - val_loss: 0.0013 - val_acc: 0.7734 Epoch 246/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9445e-04 - acc: 0.8467Epoch 00245: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9279e-04 - acc: 0.8481 - val_loss: 0.0012 - val_acc: 0.7944 Epoch 247/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8512e-04 - acc: 0.8355Epoch 00246: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8524e-04 - acc: 0.8364 - val_loss: 0.0011 - val_acc: 0.8014 Epoch 248/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8722e-04 - acc: 0.8414Epoch 00247: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8711e-04 - acc: 0.8417 - val_loss: 0.0012 - val_acc: 0.7827 Epoch 249/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.0689e-04 - acc: 0.8479Epoch 00248: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.0602e-04 - acc: 0.8464 - val_loss: 0.0011 - val_acc: 0.7921 Epoch 250/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8160e-04 - acc: 0.8485Epoch 00249: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8252e-04 - acc: 0.8464 - val_loss: 0.0011 - val_acc: 0.8014 RMSprop loss = 0.0011200990003455327 Train on 1712 samples, validate on 428 samples Epoch 1/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0702 - acc: 0.4976Epoch 00000: val_loss improved from inf to 0.02082, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0697 - acc: 0.4971 - val_loss: 0.0208 - val_acc: 0.6963 Epoch 2/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0086 - acc: 0.5902Epoch 00001: val_loss improved from 0.02082 to 0.01654, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0086 - acc: 0.5905 - val_loss: 0.0165 - val_acc: 0.6963 Epoch 3/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0078 - acc: 0.6256Epoch 00002: val_loss improved from 0.01654 to 0.01483, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0078 - acc: 0.6244 - val_loss: 0.0148 - val_acc: 0.6963 Epoch 4/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0075 - acc: 0.6315Epoch 00003: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0074 - acc: 0.6326 - val_loss: 0.0198 - val_acc: 0.6963 Epoch 5/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0071 - acc: 0.6545Epoch 00004: val_loss improved from 0.01483 to 0.01290, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0071 - acc: 0.6536 - val_loss: 0.0129 - val_acc: 0.6963 Epoch 6/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0069 - acc: 0.6504Epoch 00005: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0068 - acc: 0.6489 - val_loss: 0.0161 - val_acc: 0.6963 Epoch 7/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0066 - acc: 0.6509Epoch 00006: val_loss improved from 0.01290 to 0.01250, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0066 - acc: 0.6495 - val_loss: 0.0125 - val_acc: 0.6963 Epoch 8/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0066 - acc: 0.6551Epoch 00007: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0066 - acc: 0.6548 - val_loss: 0.0126 - val_acc: 0.6963 Epoch 9/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0065 - acc: 0.6639Epoch 00008: val_loss improved from 0.01250 to 0.01166, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0065 - acc: 0.6636 - val_loss: 0.0117 - val_acc: 0.6963 Epoch 10/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0064 - acc: 0.6669Epoch 00009: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0064 - acc: 0.6676 - val_loss: 0.0131 - val_acc: 0.6963 Epoch 11/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0064 - acc: 0.6675Epoch 00010: val_loss improved from 0.01166 to 0.01089, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0064 - acc: 0.6671 - val_loss: 0.0109 - val_acc: 0.6963 Epoch 12/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6792Epoch 00011: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6782 - val_loss: 0.0111 - val_acc: 0.6963 Epoch 13/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6869Epoch 00012: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6863 - val_loss: 0.0122 - val_acc: 0.6963 Epoch 14/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6733Epoch 00013: val_loss improved from 0.01089 to 0.01051, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6746 - val_loss: 0.0105 - val_acc: 0.6963 Epoch 15/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6863Epoch 00014: val_loss improved from 0.01051 to 0.00964, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0060 - acc: 0.6863 - val_loss: 0.0096 - val_acc: 0.6963 Epoch 16/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0061 - acc: 0.6781Epoch 00015: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0061 - acc: 0.6782 - val_loss: 0.0103 - val_acc: 0.6963 Epoch 17/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6775Epoch 00016: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6787 - val_loss: 0.0127 - val_acc: 0.6963 Epoch 18/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6887Epoch 00017: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6881 - val_loss: 0.0147 - val_acc: 0.6963 Epoch 19/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6922Epoch 00018: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6933 - val_loss: 0.0110 - val_acc: 0.6963 Epoch 20/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.6798Epoch 00019: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6811 - val_loss: 0.0107 - val_acc: 0.6963 Epoch 21/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6928Epoch 00020: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6922 - val_loss: 0.0113 - val_acc: 0.6963 Epoch 22/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6840Epoch 00021: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6852 - val_loss: 0.0107 - val_acc: 0.6963 Epoch 23/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6916Epoch 00022: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6922 - val_loss: 0.0118 - val_acc: 0.6963 Epoch 24/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6928Epoch 00023: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6939 - val_loss: 0.0115 - val_acc: 0.6963 Epoch 25/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0056 - acc: 0.6857Epoch 00024: val_loss improved from 0.00964 to 0.00954, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6852 - val_loss: 0.0095 - val_acc: 0.6963 Epoch 26/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0056 - acc: 0.6904Epoch 00025: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0056 - acc: 0.6904 - val_loss: 0.0133 - val_acc: 0.6963 Epoch 27/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0056 - acc: 0.6904Epoch 00026: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0056 - acc: 0.6893 - val_loss: 0.0109 - val_acc: 0.6963 Epoch 28/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0055 - acc: 0.6910Epoch 00027: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0056 - acc: 0.6928 - val_loss: 0.0101 - val_acc: 0.6963 Epoch 29/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0055 - acc: 0.6922Epoch 00028: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0055 - acc: 0.6928 - val_loss: 0.0109 - val_acc: 0.6963 Epoch 30/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0056 - acc: 0.6887Epoch 00029: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0056 - acc: 0.6887 - val_loss: 0.0116 - val_acc: 0.6963 Epoch 31/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0056 - acc: 0.6981Epoch 00030: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0056 - acc: 0.6968 - val_loss: 0.0105 - val_acc: 0.6963 Epoch 32/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0054 - acc: 0.6928Epoch 00031: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0054 - acc: 0.6916 - val_loss: 0.0114 - val_acc: 0.6963 Epoch 33/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0055 - acc: 0.6904Epoch 00032: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0055 - acc: 0.6904 - val_loss: 0.0098 - val_acc: 0.6963 Epoch 34/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0055 - acc: 0.6987Epoch 00033: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0055 - acc: 0.6974 - val_loss: 0.0103 - val_acc: 0.6963 Epoch 35/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0055 - acc: 0.6946Epoch 00034: val_loss improved from 0.00954 to 0.00884, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0055 - acc: 0.6933 - val_loss: 0.0088 - val_acc: 0.6963 Epoch 36/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0054 - acc: 0.7005Epoch 00035: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0054 - acc: 0.7015 - val_loss: 0.0106 - val_acc: 0.6963 Epoch 37/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0054 - acc: 0.6899Epoch 00036: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0054 - acc: 0.6904 - val_loss: 0.0116 - val_acc: 0.6963 Epoch 38/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0054 - acc: 0.6887Epoch 00037: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0054 - acc: 0.6893 - val_loss: 0.0099 - val_acc: 0.6963 Epoch 39/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0054 - acc: 0.6987Epoch 00038: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0054 - acc: 0.6968 - val_loss: 0.0105 - val_acc: 0.6963 Epoch 40/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0054 - acc: 0.6963Epoch 00039: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0054 - acc: 0.6951 - val_loss: 0.0103 - val_acc: 0.6963 Epoch 41/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0053 - acc: 0.6975Epoch 00040: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0053 - acc: 0.6951 - val_loss: 0.0100 - val_acc: 0.6963 Epoch 42/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0054 - acc: 0.6981Epoch 00041: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0053 - acc: 0.6980 - val_loss: 0.0093 - val_acc: 0.6963 Epoch 43/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0053 - acc: 0.6999Epoch 00042: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0053 - acc: 0.7004 - val_loss: 0.0095 - val_acc: 0.6963 Epoch 44/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0053 - acc: 0.7058Epoch 00043: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0054 - acc: 0.7050 - val_loss: 0.0102 - val_acc: 0.6963 Epoch 45/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0053 - acc: 0.6940Epoch 00044: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0053 - acc: 0.6933 - val_loss: 0.0096 - val_acc: 0.6963 Epoch 46/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0053 - acc: 0.6958Epoch 00045: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0053 - acc: 0.6980 - val_loss: 0.0097 - val_acc: 0.6963 Epoch 47/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.7034Epoch 00046: val_loss improved from 0.00884 to 0.00851, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0052 - acc: 0.7033 - val_loss: 0.0085 - val_acc: 0.6963 Epoch 48/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0053 - acc: 0.7028Epoch 00047: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0053 - acc: 0.7015 - val_loss: 0.0104 - val_acc: 0.6963 Epoch 49/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.7028Epoch 00048: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0052 - acc: 0.7015 - val_loss: 0.0095 - val_acc: 0.6963 Epoch 50/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0053 - acc: 0.7040Epoch 00049: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0053 - acc: 0.7033 - val_loss: 0.0097 - val_acc: 0.6963 Epoch 51/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.6958Epoch 00050: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0052 - acc: 0.6945 - val_loss: 0.0086 - val_acc: 0.6963 Epoch 52/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.7028Epoch 00051: val_loss improved from 0.00851 to 0.00848, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0052 - acc: 0.7027 - val_loss: 0.0085 - val_acc: 0.6963 Epoch 53/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.7046Epoch 00052: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0053 - acc: 0.7033 - val_loss: 0.0095 - val_acc: 0.6963 Epoch 54/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.6999Epoch 00053: val_loss improved from 0.00848 to 0.00821, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0052 - acc: 0.7004 - val_loss: 0.0082 - val_acc: 0.6963 Epoch 55/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.6969Epoch 00054: val_loss improved from 0.00821 to 0.00804, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.6968 - val_loss: 0.0080 - val_acc: 0.6963 Epoch 56/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.6981Epoch 00055: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0052 - acc: 0.6980 - val_loss: 0.0085 - val_acc: 0.6963 Epoch 57/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.6999Epoch 00056: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.7009 - val_loss: 0.0086 - val_acc: 0.6963 Epoch 58/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.7034Epoch 00057: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0052 - acc: 0.7009 - val_loss: 0.0091 - val_acc: 0.6963 Epoch 59/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.7064Epoch 00058: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0052 - acc: 0.7050 - val_loss: 0.0090 - val_acc: 0.6963 Epoch 60/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.7064Epoch 00059: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.7074 - val_loss: 0.0082 - val_acc: 0.6963 Epoch 61/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.7040Epoch 00060: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0052 - acc: 0.7044 - val_loss: 0.0088 - val_acc: 0.6963 Epoch 62/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.7011Epoch 00061: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.7015 - val_loss: 0.0084 - val_acc: 0.6963 Epoch 63/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.7028Epoch 00062: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0052 - acc: 0.7039 - val_loss: 0.0088 - val_acc: 0.6963 Epoch 64/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.6999Epoch 00063: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.6998 - val_loss: 0.0082 - val_acc: 0.6963 Epoch 65/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.7017Epoch 00064: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.7015 - val_loss: 0.0083 - val_acc: 0.6963 Epoch 66/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.7022Epoch 00065: val_loss improved from 0.00804 to 0.00751, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.7021 - val_loss: 0.0075 - val_acc: 0.6963 Epoch 67/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.6981Epoch 00066: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.6986 - val_loss: 0.0084 - val_acc: 0.6963 Epoch 68/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.7022Epoch 00067: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.7021 - val_loss: 0.0085 - val_acc: 0.6963 Epoch 69/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.7028Epoch 00068: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.7033 - val_loss: 0.0079 - val_acc: 0.6963 Epoch 70/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.7028Epoch 00069: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7015 - val_loss: 0.0076 - val_acc: 0.6963 Epoch 71/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.6999Epoch 00070: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.7009 - val_loss: 0.0076 - val_acc: 0.6963 Epoch 72/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.7052Epoch 00071: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7044 - val_loss: 0.0077 - val_acc: 0.6963 Epoch 73/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.7022Epoch 00072: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.7033 - val_loss: 0.0078 - val_acc: 0.6963 Epoch 74/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.7034Epoch 00073: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7027 - val_loss: 0.0076 - val_acc: 0.6963 Epoch 75/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.7017Epoch 00074: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7033 - val_loss: 0.0076 - val_acc: 0.6963 Epoch 76/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.7040Epoch 00075: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.7039 - val_loss: 0.0081 - val_acc: 0.6963 Epoch 77/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.7052Epoch 00076: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.7044 - val_loss: 0.0077 - val_acc: 0.6963 Epoch 78/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.7075Epoch 00077: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.7039 - val_loss: 0.0078 - val_acc: 0.6963 Epoch 79/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.7046Epoch 00078: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7027 - val_loss: 0.0075 - val_acc: 0.6963 Epoch 80/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.7070Epoch 00079: val_loss improved from 0.00751 to 0.00745, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.7068 - val_loss: 0.0075 - val_acc: 0.6963 Epoch 81/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.6969Epoch 00080: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.6980 - val_loss: 0.0076 - val_acc: 0.6963 Epoch 82/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.7040Epoch 00081: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7015 - val_loss: 0.0076 - val_acc: 0.6963 Epoch 83/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.7093Epoch 00082: val_loss improved from 0.00745 to 0.00729, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7068 - val_loss: 0.0073 - val_acc: 0.6963 Epoch 84/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.6999Epoch 00083: val_loss improved from 0.00729 to 0.00719, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7015 - val_loss: 0.0072 - val_acc: 0.6963 Epoch 85/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7046Epoch 00084: val_loss improved from 0.00719 to 0.00717, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7039 - val_loss: 0.0072 - val_acc: 0.6963 Epoch 86/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.7028Epoch 00085: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7033 - val_loss: 0.0076 - val_acc: 0.6963 Epoch 87/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.7040Epoch 00086: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7056 - val_loss: 0.0072 - val_acc: 0.6963 Epoch 88/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.7011Epoch 00087: val_loss improved from 0.00717 to 0.00717, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7021 - val_loss: 0.0072 - val_acc: 0.6963 Epoch 89/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.7075Epoch 00088: val_loss improved from 0.00717 to 0.00666, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7068 - val_loss: 0.0067 - val_acc: 0.6963 Epoch 90/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.7034Epoch 00089: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7050 - val_loss: 0.0071 - val_acc: 0.6963 Epoch 91/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7028Epoch 00090: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7033 - val_loss: 0.0070 - val_acc: 0.6963 Epoch 92/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.7040Epoch 00091: val_loss improved from 0.00666 to 0.00658, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7033 - val_loss: 0.0066 - val_acc: 0.6963 Epoch 93/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7034Epoch 00092: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7033 - val_loss: 0.0067 - val_acc: 0.6963 Epoch 94/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7028Epoch 00093: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7027 - val_loss: 0.0067 - val_acc: 0.6963 Epoch 95/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.7022Epoch 00094: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7050 - val_loss: 0.0067 - val_acc: 0.6963 Epoch 96/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7070Epoch 00095: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7044 - val_loss: 0.0069 - val_acc: 0.6963 Epoch 97/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7075Epoch 00096: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7056 - val_loss: 0.0071 - val_acc: 0.6963 Epoch 98/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.7070Epoch 00097: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7068 - val_loss: 0.0071 - val_acc: 0.6963 Epoch 99/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7087Epoch 00098: val_loss improved from 0.00658 to 0.00643, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7079 - val_loss: 0.0064 - val_acc: 0.6963 Epoch 100/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7022Epoch 00099: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7021 - val_loss: 0.0066 - val_acc: 0.6963 Epoch 101/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7064Epoch 00100: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7039 - val_loss: 0.0071 - val_acc: 0.6963 Epoch 102/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7087Epoch 00101: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7074 - val_loss: 0.0065 - val_acc: 0.6963 Epoch 103/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7040Epoch 00102: val_loss improved from 0.00643 to 0.00642, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7050 - val_loss: 0.0064 - val_acc: 0.6963 Epoch 104/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7064Epoch 00103: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7062 - val_loss: 0.0067 - val_acc: 0.6963 Epoch 105/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7046Epoch 00104: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7056 - val_loss: 0.0065 - val_acc: 0.6963 Epoch 106/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7040Epoch 00105: val_loss improved from 0.00642 to 0.00641, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7050 - val_loss: 0.0064 - val_acc: 0.6963 Epoch 107/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7046Epoch 00106: val_loss improved from 0.00641 to 0.00638, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7056 - val_loss: 0.0064 - val_acc: 0.6963 Epoch 108/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7064Epoch 00107: val_loss improved from 0.00638 to 0.00619, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7062 - val_loss: 0.0062 - val_acc: 0.6963 Epoch 109/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7034Epoch 00108: val_loss improved from 0.00619 to 0.00615, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7033 - val_loss: 0.0061 - val_acc: 0.6963 Epoch 110/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7046Epoch 00109: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7044 - val_loss: 0.0063 - val_acc: 0.6963 Epoch 111/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7087Epoch 00110: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7079 - val_loss: 0.0063 - val_acc: 0.6963 Epoch 112/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7064Epoch 00111: val_loss improved from 0.00615 to 0.00585, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7056 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 113/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7064Epoch 00112: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7068 - val_loss: 0.0063 - val_acc: 0.6963 Epoch 114/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7081Epoch 00113: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7074 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 115/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7040Epoch 00114: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7033 - val_loss: 0.0061 - val_acc: 0.6963 Epoch 116/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7087Epoch 00115: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7085 - val_loss: 0.0061 - val_acc: 0.6963 Epoch 117/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7064Epoch 00116: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7062 - val_loss: 0.0062 - val_acc: 0.6963 Epoch 118/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7064Epoch 00117: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7050 - val_loss: 0.0062 - val_acc: 0.6963 Epoch 119/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7052Epoch 00118: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7062 - val_loss: 0.0061 - val_acc: 0.6963 Epoch 120/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7064Epoch 00119: val_loss improved from 0.00585 to 0.00562, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7074 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 121/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7075Epoch 00120: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7079 - val_loss: 0.0063 - val_acc: 0.6963 Epoch 122/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7040Epoch 00121: val_loss improved from 0.00562 to 0.00561, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7044 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 123/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7070Epoch 00122: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7056 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 124/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7105Epoch 00123: val_loss improved from 0.00561 to 0.00537, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7079 - val_loss: 0.0054 - val_acc: 0.6963 Epoch 125/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7034Epoch 00124: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7039 - val_loss: 0.0059 - val_acc: 0.6963 Epoch 126/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7046Epoch 00125: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7039 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 127/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7058Epoch 00126: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7062 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 128/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7046Epoch 00127: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7033 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 129/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7052Epoch 00128: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7044 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 130/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7105Epoch 00129: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7091 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 131/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7064Epoch 00130: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7056 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 132/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7052Epoch 00131: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7062 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 133/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7081Epoch 00132: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7085 - val_loss: 0.0059 - val_acc: 0.6963 Epoch 134/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7058Epoch 00133: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7062 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 135/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7064Epoch 00134: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7068 - val_loss: 0.0054 - val_acc: 0.6963 Epoch 136/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7058Epoch 00135: val_loss improved from 0.00537 to 0.00536, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7062 - val_loss: 0.0054 - val_acc: 0.6963 Epoch 137/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7040Epoch 00136: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7033 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 138/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7058Epoch 00137: val_loss improved from 0.00536 to 0.00529, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7056 - val_loss: 0.0053 - val_acc: 0.6963 Epoch 139/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7058Epoch 00138: val_loss improved from 0.00529 to 0.00494, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7062 - val_loss: 0.0049 - val_acc: 0.6963 Epoch 140/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7070Epoch 00139: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7068 - val_loss: 0.0054 - val_acc: 0.6963 Epoch 141/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7052Epoch 00140: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7056 - val_loss: 0.0051 - val_acc: 0.6963 Epoch 142/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7058Epoch 00141: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7050 - val_loss: 0.0054 - val_acc: 0.6963 Epoch 143/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7087Epoch 00142: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7085 - val_loss: 0.0051 - val_acc: 0.6963 Epoch 144/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7064Epoch 00143: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7062 - val_loss: 0.0050 - val_acc: 0.6963 Epoch 145/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7064Epoch 00144: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7074 - val_loss: 0.0050 - val_acc: 0.6963 Epoch 146/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7046Epoch 00145: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7044 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 147/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7052Epoch 00146: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7044 - val_loss: 0.0050 - val_acc: 0.6963 Epoch 148/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7040Epoch 00147: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7039 - val_loss: 0.0053 - val_acc: 0.6963 Epoch 149/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7034Epoch 00148: val_loss improved from 0.00494 to 0.00493, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7027 - val_loss: 0.0049 - val_acc: 0.6963 Epoch 150/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7087Epoch 00149: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7074 - val_loss: 0.0050 - val_acc: 0.6963 Epoch 151/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7070Epoch 00150: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7068 - val_loss: 0.0050 - val_acc: 0.6963 Epoch 152/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7040Epoch 00151: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7044 - val_loss: 0.0051 - val_acc: 0.6963 Epoch 153/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7081Epoch 00152: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7079 - val_loss: 0.0050 - val_acc: 0.6963 Epoch 154/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7052Epoch 00153: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7056 - val_loss: 0.0051 - val_acc: 0.6963 Epoch 155/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7052Epoch 00154: val_loss improved from 0.00493 to 0.00482, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7039 - val_loss: 0.0048 - val_acc: 0.6963 Epoch 156/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7052Epoch 00155: val_loss improved from 0.00482 to 0.00479, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7050 - val_loss: 0.0048 - val_acc: 0.6963 Epoch 157/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7058Epoch 00156: val_loss improved from 0.00479 to 0.00470, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7062 - val_loss: 0.0047 - val_acc: 0.6963 Epoch 158/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7034Epoch 00157: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7056 - val_loss: 0.0048 - val_acc: 0.6963 Epoch 159/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7022Epoch 00158: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7027 - val_loss: 0.0049 - val_acc: 0.6963 Epoch 160/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7070Epoch 00159: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7062 - val_loss: 0.0050 - val_acc: 0.6963 Epoch 161/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7028Epoch 00160: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7027 - val_loss: 0.0047 - val_acc: 0.6963 Epoch 162/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7081Epoch 00161: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7079 - val_loss: 0.0048 - val_acc: 0.6963 Epoch 163/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7064Epoch 00162: val_loss improved from 0.00470 to 0.00466, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7074 - val_loss: 0.0047 - val_acc: 0.6963 Epoch 164/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7075Epoch 00163: val_loss improved from 0.00466 to 0.00451, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7085 - val_loss: 0.0045 - val_acc: 0.6963 Epoch 165/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7064Epoch 00164: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7068 - val_loss: 0.0048 - val_acc: 0.6963 Epoch 166/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7028Epoch 00165: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7021 - val_loss: 0.0046 - val_acc: 0.6963 Epoch 167/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7017Epoch 00166: val_loss improved from 0.00451 to 0.00436, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0039 - acc: 0.7027 - val_loss: 0.0044 - val_acc: 0.6963 Epoch 168/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0038 - acc: 0.7022Epoch 00167: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0038 - acc: 0.7050 - val_loss: 0.0045 - val_acc: 0.6963 Epoch 169/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0038 - acc: 0.7052Epoch 00168: val_loss improved from 0.00436 to 0.00415, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0039 - acc: 0.7050 - val_loss: 0.0041 - val_acc: 0.6963 Epoch 170/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0037 - acc: 0.7005Epoch 00169: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0038 - acc: 0.6998 - val_loss: 0.0045 - val_acc: 0.6963 Epoch 171/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0037 - acc: 0.7011Epoch 00170: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0037 - acc: 0.7015 - val_loss: 0.0043 - val_acc: 0.6963 Epoch 172/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0037 - acc: 0.7052Epoch 00171: val_loss improved from 0.00415 to 0.00403, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0037 - acc: 0.7044 - val_loss: 0.0040 - val_acc: 0.6963 Epoch 173/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0036 - acc: 0.6981Epoch 00172: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0036 - acc: 0.6980 - val_loss: 0.0041 - val_acc: 0.6963 Epoch 174/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0035 - acc: 0.6999Epoch 00173: val_loss improved from 0.00403 to 0.00377, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0036 - acc: 0.7009 - val_loss: 0.0038 - val_acc: 0.6963 Epoch 175/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0035 - acc: 0.7087Epoch 00174: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0035 - acc: 0.7079 - val_loss: 0.0039 - val_acc: 0.6963 Epoch 176/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0034 - acc: 0.7052Epoch 00175: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0034 - acc: 0.7056 - val_loss: 0.0042 - val_acc: 0.6963 Epoch 177/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0033 - acc: 0.7064Epoch 00176: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0033 - acc: 0.7062 - val_loss: 0.0038 - val_acc: 0.7009 Epoch 178/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0033 - acc: 0.6963Epoch 00177: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0033 - acc: 0.6963 - val_loss: 0.0040 - val_acc: 0.7009 Epoch 179/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0033 - acc: 0.7070Epoch 00178: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0033 - acc: 0.7062 - val_loss: 0.0039 - val_acc: 0.6986 Epoch 180/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0033 - acc: 0.7028Epoch 00179: val_loss improved from 0.00377 to 0.00362, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0033 - acc: 0.7050 - val_loss: 0.0036 - val_acc: 0.6986 Epoch 181/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0033 - acc: 0.7064Epoch 00180: val_loss improved from 0.00362 to 0.00349, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0033 - acc: 0.7074 - val_loss: 0.0035 - val_acc: 0.6986 Epoch 182/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0032 - acc: 0.7028Epoch 00181: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0032 - acc: 0.7021 - val_loss: 0.0036 - val_acc: 0.6986 Epoch 183/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0031 - acc: 0.7093Epoch 00182: val_loss improved from 0.00349 to 0.00347, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0031 - acc: 0.7074 - val_loss: 0.0035 - val_acc: 0.7009 Epoch 184/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0031 - acc: 0.7022Epoch 00183: val_loss improved from 0.00347 to 0.00322, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0031 - acc: 0.7039 - val_loss: 0.0032 - val_acc: 0.7009 Epoch 185/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0031 - acc: 0.7152Epoch 00184: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0031 - acc: 0.7161 - val_loss: 0.0035 - val_acc: 0.7009 Epoch 186/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0030 - acc: 0.7158Epoch 00185: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0030 - acc: 0.7173 - val_loss: 0.0034 - val_acc: 0.6986 Epoch 187/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0030 - acc: 0.7099Epoch 00186: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0030 - acc: 0.7109 - val_loss: 0.0033 - val_acc: 0.6986 Epoch 188/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0030 - acc: 0.7075Epoch 00187: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0030 - acc: 0.7085 - val_loss: 0.0038 - val_acc: 0.7056 Epoch 189/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0029 - acc: 0.7070Epoch 00188: val_loss improved from 0.00322 to 0.00313, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0029 - acc: 0.7074 - val_loss: 0.0031 - val_acc: 0.7103 Epoch 190/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0029 - acc: 0.7188Epoch 00189: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0029 - acc: 0.7190 - val_loss: 0.0035 - val_acc: 0.7173 Epoch 191/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0029 - acc: 0.7105Epoch 00190: val_loss improved from 0.00313 to 0.00313, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0029 - acc: 0.7120 - val_loss: 0.0031 - val_acc: 0.7079 Epoch 192/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0029 - acc: 0.7022Epoch 00191: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0028 - acc: 0.7021 - val_loss: 0.0035 - val_acc: 0.7196 Epoch 193/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0028 - acc: 0.7075Epoch 00192: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0028 - acc: 0.7068 - val_loss: 0.0033 - val_acc: 0.7103 Epoch 194/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0028 - acc: 0.7005Epoch 00193: val_loss improved from 0.00313 to 0.00312, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0028 - acc: 0.7021 - val_loss: 0.0031 - val_acc: 0.7079 Epoch 195/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0028 - acc: 0.7134Epoch 00194: val_loss improved from 0.00312 to 0.00302, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0028 - acc: 0.7120 - val_loss: 0.0030 - val_acc: 0.7243 Epoch 196/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0027 - acc: 0.7105Epoch 00195: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0027 - acc: 0.7097 - val_loss: 0.0033 - val_acc: 0.7220 Epoch 197/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0027 - acc: 0.7158Epoch 00196: val_loss improved from 0.00302 to 0.00278, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0027 - acc: 0.7161 - val_loss: 0.0028 - val_acc: 0.7173 Epoch 198/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0027 - acc: 0.7193Epoch 00197: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0027 - acc: 0.7202 - val_loss: 0.0031 - val_acc: 0.7173 Epoch 199/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0027 - acc: 0.7099Epoch 00198: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0027 - acc: 0.7091 - val_loss: 0.0030 - val_acc: 0.7266 Epoch 200/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0026 - acc: 0.7058Epoch 00199: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0027 - acc: 0.7044 - val_loss: 0.0030 - val_acc: 0.7126 Epoch 201/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0026 - acc: 0.7176Epoch 00200: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0026 - acc: 0.7179 - val_loss: 0.0028 - val_acc: 0.7173 Epoch 202/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0027 - acc: 0.7211Epoch 00201: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0026 - acc: 0.7231 - val_loss: 0.0030 - val_acc: 0.7173 Epoch 203/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0026 - acc: 0.7176Epoch 00202: val_loss improved from 0.00278 to 0.00277, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0026 - acc: 0.7179 - val_loss: 0.0028 - val_acc: 0.7290 Epoch 204/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0026 - acc: 0.7123Epoch 00203: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0026 - acc: 0.7114 - val_loss: 0.0031 - val_acc: 0.7126 Epoch 205/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0026 - acc: 0.7223Epoch 00204: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0026 - acc: 0.7237 - val_loss: 0.0028 - val_acc: 0.7243 Epoch 206/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0025 - acc: 0.7199Epoch 00205: val_loss improved from 0.00277 to 0.00269, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0025 - acc: 0.7196 - val_loss: 0.0027 - val_acc: 0.7150 Epoch 207/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0025 - acc: 0.7199Epoch 00206: val_loss improved from 0.00269 to 0.00253, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0025 - acc: 0.7214 - val_loss: 0.0025 - val_acc: 0.7243 Epoch 208/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0025 - acc: 0.7252Epoch 00207: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0025 - acc: 0.7231 - val_loss: 0.0027 - val_acc: 0.7290 Epoch 209/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0025 - acc: 0.7353Epoch 00208: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0025 - acc: 0.7342 - val_loss: 0.0027 - val_acc: 0.7266 Epoch 210/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0024 - acc: 0.7317Epoch 00209: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0024 - acc: 0.7336 - val_loss: 0.0026 - val_acc: 0.7079 Epoch 211/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0024 - acc: 0.7246Epoch 00210: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0024 - acc: 0.7243 - val_loss: 0.0026 - val_acc: 0.7243 Epoch 212/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0024 - acc: 0.7176Epoch 00211: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0024 - acc: 0.7190 - val_loss: 0.0026 - val_acc: 0.7126 Epoch 213/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0024 - acc: 0.7423Epoch 00212: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0024 - acc: 0.7424 - val_loss: 0.0029 - val_acc: 0.7173 Epoch 214/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0024 - acc: 0.7305Epoch 00213: val_loss improved from 0.00253 to 0.00251, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0024 - acc: 0.7307 - val_loss: 0.0025 - val_acc: 0.7243 Epoch 215/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0023 - acc: 0.7353Epoch 00214: val_loss improved from 0.00251 to 0.00236, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0023 - acc: 0.7336 - val_loss: 0.0024 - val_acc: 0.7220 Epoch 216/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0024 - acc: 0.7388Epoch 00215: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0024 - acc: 0.7371 - val_loss: 0.0024 - val_acc: 0.7290 Epoch 217/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0023 - acc: 0.7376Epoch 00216: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0023 - acc: 0.7360 - val_loss: 0.0026 - val_acc: 0.7266 Epoch 218/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0024 - acc: 0.7353Epoch 00217: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0024 - acc: 0.7342 - val_loss: 0.0025 - val_acc: 0.7173 Epoch 219/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0023 - acc: 0.7459Epoch 00218: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0023 - acc: 0.7459 - val_loss: 0.0028 - val_acc: 0.7220 Epoch 220/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0023 - acc: 0.7400Epoch 00219: val_loss improved from 0.00236 to 0.00229, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0023 - acc: 0.7401 - val_loss: 0.0023 - val_acc: 0.7173 Epoch 221/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7193Epoch 00220: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7185 - val_loss: 0.0025 - val_acc: 0.7266 Epoch 222/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7347Epoch 00221: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7354 - val_loss: 0.0026 - val_acc: 0.7290 Epoch 223/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0023 - acc: 0.7353Epoch 00222: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0023 - acc: 0.7354 - val_loss: 0.0024 - val_acc: 0.7290 Epoch 224/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7394Epoch 00223: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7389 - val_loss: 0.0025 - val_acc: 0.7360 Epoch 225/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7353Epoch 00224: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7371 - val_loss: 0.0024 - val_acc: 0.7196 Epoch 226/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7241Epoch 00225: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7261 - val_loss: 0.0023 - val_acc: 0.7196 Epoch 227/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7353Epoch 00226: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7342 - val_loss: 0.0023 - val_acc: 0.7313 Epoch 228/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7335Epoch 00227: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7336 - val_loss: 0.0024 - val_acc: 0.7266 Epoch 229/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7417Epoch 00228: val_loss improved from 0.00229 to 0.00223, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7424 - val_loss: 0.0022 - val_acc: 0.7173 Epoch 230/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7447Epoch 00229: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7453 - val_loss: 0.0024 - val_acc: 0.7290 Epoch 231/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7364Epoch 00230: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7383 - val_loss: 0.0024 - val_acc: 0.7150 Epoch 232/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7288Epoch 00231: val_loss improved from 0.00223 to 0.00221, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7296 - val_loss: 0.0022 - val_acc: 0.7173 Epoch 233/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0021 - acc: 0.7518Epoch 00232: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0021 - acc: 0.7535 - val_loss: 0.0023 - val_acc: 0.7266 Epoch 234/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7441Epoch 00233: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7447 - val_loss: 0.0022 - val_acc: 0.7336 Epoch 235/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0021 - acc: 0.7447Epoch 00234: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0021 - acc: 0.7453 - val_loss: 0.0024 - val_acc: 0.7360 Epoch 236/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0021 - acc: 0.7406Epoch 00235: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0021 - acc: 0.7418 - val_loss: 0.0024 - val_acc: 0.7313 Epoch 237/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0021 - acc: 0.7471Epoch 00236: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0021 - acc: 0.7465 - val_loss: 0.0023 - val_acc: 0.7313 Epoch 238/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0021 - acc: 0.7423Epoch 00237: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0021 - acc: 0.7424 - val_loss: 0.0025 - val_acc: 0.7336 Epoch 239/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0020 - acc: 0.7535Epoch 00238: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0021 - acc: 0.7518 - val_loss: 0.0025 - val_acc: 0.7336 Epoch 240/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0021 - acc: 0.7435Epoch 00239: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0021 - acc: 0.7442 - val_loss: 0.0022 - val_acc: 0.7360 Epoch 241/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0020 - acc: 0.7406Epoch 00240: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0020 - acc: 0.7418 - val_loss: 0.0023 - val_acc: 0.7266 Epoch 242/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0021 - acc: 0.7417Epoch 00241: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0021 - acc: 0.7418 - val_loss: 0.0022 - val_acc: 0.7453 Epoch 243/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0021 - acc: 0.7529Epoch 00242: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0020 - acc: 0.7529 - val_loss: 0.0023 - val_acc: 0.7360 Epoch 244/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0021 - acc: 0.7506Epoch 00243: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0021 - acc: 0.7494 - val_loss: 0.0023 - val_acc: 0.7266 Epoch 245/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0020 - acc: 0.7423Epoch 00244: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0020 - acc: 0.7407 - val_loss: 0.0022 - val_acc: 0.7360 Epoch 246/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0020 - acc: 0.7488Epoch 00245: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0020 - acc: 0.7477 - val_loss: 0.0022 - val_acc: 0.7243 Epoch 247/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0020 - acc: 0.7471Epoch 00246: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0020 - acc: 0.7482 - val_loss: 0.0024 - val_acc: 0.7313 Epoch 248/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0020 - acc: 0.7447Epoch 00247: val_loss improved from 0.00221 to 0.00217, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0020 - acc: 0.7465 - val_loss: 0.0022 - val_acc: 0.7266 Epoch 249/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0020 - acc: 0.7647Epoch 00248: val_loss improved from 0.00217 to 0.00206, saving model to my_model_Adagrad.h5 1712/1712 [==============================] - 3s - loss: 0.0021 - acc: 0.7640 - val_loss: 0.0021 - val_acc: 0.7360 Epoch 250/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0020 - acc: 0.7535Epoch 00249: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0020 - acc: 0.7535 - val_loss: 0.0021 - val_acc: 0.7313 Adagrad loss = 0.002141338068051873 Train on 1712 samples, validate on 428 samples Epoch 1/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0231 - acc: 0.5324Epoch 00000: val_loss improved from inf to 0.08761, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0230 - acc: 0.5333 - val_loss: 0.0876 - val_acc: 0.6963 Epoch 2/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0112 - acc: 0.5790Epoch 00001: val_loss improved from 0.08761 to 0.07888, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0113 - acc: 0.5794 - val_loss: 0.0789 - val_acc: 0.6963 Epoch 3/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0085 - acc: 0.6120Epoch 00002: val_loss improved from 0.07888 to 0.06028, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0085 - acc: 0.6133 - val_loss: 0.0603 - val_acc: 0.6963 Epoch 4/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0076 - acc: 0.6256Epoch 00003: val_loss improved from 0.06028 to 0.05448, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0076 - acc: 0.6262 - val_loss: 0.0545 - val_acc: 0.6963 Epoch 5/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0072 - acc: 0.6327Epoch 00004: val_loss improved from 0.05448 to 0.04264, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0071 - acc: 0.6349 - val_loss: 0.0426 - val_acc: 0.6963 Epoch 6/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0066 - acc: 0.6492Epoch 00005: val_loss improved from 0.04264 to 0.04158, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0065 - acc: 0.6519 - val_loss: 0.0416 - val_acc: 0.6963 Epoch 7/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0064 - acc: 0.6568Epoch 00006: val_loss improved from 0.04158 to 0.03325, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0063 - acc: 0.6583 - val_loss: 0.0333 - val_acc: 0.6963 Epoch 8/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0062 - acc: 0.6633Epoch 00007: val_loss improved from 0.03325 to 0.03013, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6641 - val_loss: 0.0301 - val_acc: 0.6963 Epoch 9/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0060 - acc: 0.6586Epoch 00008: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0059 - acc: 0.6595 - val_loss: 0.0307 - val_acc: 0.6963 Epoch 10/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6704Epoch 00009: val_loss improved from 0.03013 to 0.02657, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6711 - val_loss: 0.0266 - val_acc: 0.6963 Epoch 11/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6722Epoch 00010: val_loss improved from 0.02657 to 0.02477, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0056 - acc: 0.6729 - val_loss: 0.0248 - val_acc: 0.6963 Epoch 12/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0056 - acc: 0.6769Epoch 00011: val_loss improved from 0.02477 to 0.01939, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0056 - acc: 0.6770 - val_loss: 0.0194 - val_acc: 0.6963 Epoch 13/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0056 - acc: 0.6751Epoch 00012: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0056 - acc: 0.6752 - val_loss: 0.0216 - val_acc: 0.6963 Epoch 14/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0054 - acc: 0.6840Epoch 00013: val_loss improved from 0.01939 to 0.01795, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0054 - acc: 0.6840 - val_loss: 0.0180 - val_acc: 0.6963 Epoch 15/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0054 - acc: 0.6810Epoch 00014: val_loss improved from 0.01795 to 0.01532, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0053 - acc: 0.6799 - val_loss: 0.0153 - val_acc: 0.6963 Epoch 16/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0053 - acc: 0.6916Epoch 00015: val_loss improved from 0.01532 to 0.01425, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0053 - acc: 0.6916 - val_loss: 0.0143 - val_acc: 0.6963 Epoch 17/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0053 - acc: 0.6928Epoch 00016: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0053 - acc: 0.6939 - val_loss: 0.0147 - val_acc: 0.6963 Epoch 18/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.6958Epoch 00017: val_loss improved from 0.01425 to 0.01307, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0052 - acc: 0.6968 - val_loss: 0.0131 - val_acc: 0.6963 Epoch 19/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.6946Epoch 00018: val_loss improved from 0.01307 to 0.01179, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0052 - acc: 0.6928 - val_loss: 0.0118 - val_acc: 0.6963 Epoch 20/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.6969Epoch 00019: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.6968 - val_loss: 0.0138 - val_acc: 0.6963 Epoch 21/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.6987Epoch 00020: val_loss improved from 0.01179 to 0.00991, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.6992 - val_loss: 0.0099 - val_acc: 0.6963 Epoch 22/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.6946Epoch 00021: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.6963 - val_loss: 0.0108 - val_acc: 0.6963 Epoch 23/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.6999Epoch 00022: val_loss improved from 0.00991 to 0.00923, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.6986 - val_loss: 0.0092 - val_acc: 0.6963 Epoch 24/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.6928Epoch 00023: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.6939 - val_loss: 0.0095 - val_acc: 0.6963 Epoch 25/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.6981Epoch 00024: val_loss improved from 0.00923 to 0.00923, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.6986 - val_loss: 0.0092 - val_acc: 0.6963 Epoch 26/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.7064Epoch 00025: val_loss improved from 0.00923 to 0.00880, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7062 - val_loss: 0.0088 - val_acc: 0.6963 Epoch 27/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.7005Epoch 00026: val_loss improved from 0.00880 to 0.00685, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7009 - val_loss: 0.0068 - val_acc: 0.6963 Epoch 28/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.7064Epoch 00027: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7033 - val_loss: 0.0083 - val_acc: 0.6963 Epoch 29/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7034Epoch 00028: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.7027 - val_loss: 0.0088 - val_acc: 0.6963 Epoch 30/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.6952Epoch 00029: val_loss improved from 0.00685 to 0.00675, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.6974 - val_loss: 0.0068 - val_acc: 0.6963 Epoch 31/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7052Epoch 00030: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7033 - val_loss: 0.0075 - val_acc: 0.6963 Epoch 32/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7093Epoch 00031: val_loss improved from 0.00675 to 0.00647, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7068 - val_loss: 0.0065 - val_acc: 0.6963 Epoch 33/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7017Epoch 00032: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7015 - val_loss: 0.0072 - val_acc: 0.6963 Epoch 34/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7058Epoch 00033: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7068 - val_loss: 0.0070 - val_acc: 0.6963 Epoch 35/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7070Epoch 00034: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7050 - val_loss: 0.0074 - val_acc: 0.6963 Epoch 36/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7005Epoch 00035: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7009 - val_loss: 0.0066 - val_acc: 0.6963 Epoch 37/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7064Epoch 00036: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7074 - val_loss: 0.0067 - val_acc: 0.6963 Epoch 38/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7046Epoch 00037: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7039 - val_loss: 0.0066 - val_acc: 0.6963 Epoch 39/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7070Epoch 00038: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7062 - val_loss: 0.0067 - val_acc: 0.6963 Epoch 40/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7070Epoch 00039: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7044 - val_loss: 0.0067 - val_acc: 0.6963 Epoch 41/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7105Epoch 00040: val_loss improved from 0.00647 to 0.00584, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7085 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 42/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7064Epoch 00041: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7079 - val_loss: 0.0064 - val_acc: 0.6963 Epoch 43/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7040Epoch 00042: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7044 - val_loss: 0.0061 - val_acc: 0.6963 Epoch 44/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7017Epoch 00043: val_loss improved from 0.00584 to 0.00547, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7027 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 45/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7075Epoch 00044: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7056 - val_loss: 0.0061 - val_acc: 0.6963 Epoch 46/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7028Epoch 00045: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7033 - val_loss: 0.0069 - val_acc: 0.6963 Epoch 47/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7040Epoch 00046: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7044 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 48/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7034Epoch 00047: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7039 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 49/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7070Epoch 00048: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7074 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 50/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7046Epoch 00049: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7062 - val_loss: 0.0063 - val_acc: 0.6963 Epoch 51/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7034Epoch 00050: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7039 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 52/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7070Epoch 00051: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7062 - val_loss: 0.0059 - val_acc: 0.6963 Epoch 53/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7081Epoch 00052: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7074 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 54/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7052Epoch 00053: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7044 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 55/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7075Epoch 00054: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7056 - val_loss: 0.0059 - val_acc: 0.6963 Epoch 56/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7075Epoch 00055: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7085 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 57/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7087Epoch 00056: val_loss improved from 0.00547 to 0.00518, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7074 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 58/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7064Epoch 00057: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7062 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 59/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7070Epoch 00058: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7074 - val_loss: 0.0059 - val_acc: 0.6963 Epoch 60/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7064Epoch 00059: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7062 - val_loss: 0.0059 - val_acc: 0.6963 Epoch 61/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7075Epoch 00060: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7074 - val_loss: 0.0053 - val_acc: 0.6963 Epoch 62/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7046Epoch 00061: val_loss improved from 0.00518 to 0.00515, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7056 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 63/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7087Epoch 00062: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7085 - val_loss: 0.0054 - val_acc: 0.6963 Epoch 64/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7064Epoch 00063: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7062 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 65/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7040Epoch 00064: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7062 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 66/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7058Epoch 00065: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7062 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 67/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7087Epoch 00066: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7074 - val_loss: 0.0054 - val_acc: 0.6963 Epoch 68/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7034Epoch 00067: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7050 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 69/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7081Epoch 00068: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7074 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 70/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7070Epoch 00069: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7068 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 71/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7070Epoch 00070: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7074 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 72/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7070Epoch 00071: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7074 - val_loss: 0.0053 - val_acc: 0.6963 Epoch 73/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7058Epoch 00072: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7062 - val_loss: 0.0054 - val_acc: 0.6963 Epoch 74/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7070Epoch 00073: val_loss improved from 0.00515 to 0.00499, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7068 - val_loss: 0.0050 - val_acc: 0.6963 Epoch 75/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7058Epoch 00074: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7074 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 76/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7046Epoch 00075: val_loss improved from 0.00499 to 0.00492, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7056 - val_loss: 0.0049 - val_acc: 0.6963 Epoch 77/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7070Epoch 00076: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7074 - val_loss: 0.0053 - val_acc: 0.6963 Epoch 78/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7093Epoch 00077: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7074 - val_loss: 0.0053 - val_acc: 0.6963 Epoch 79/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7075Epoch 00078: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7074 - val_loss: 0.0059 - val_acc: 0.6963 Epoch 80/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7058Epoch 00079: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7062 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 81/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7058Epoch 00080: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7068 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 82/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7070Epoch 00081: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7074 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 83/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7064Epoch 00082: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7068 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 84/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7064Epoch 00083: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7074 - val_loss: 0.0054 - val_acc: 0.6963 Epoch 85/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7070Epoch 00084: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7068 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 86/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7064Epoch 00085: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7068 - val_loss: 0.0054 - val_acc: 0.6963 Epoch 87/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7075Epoch 00086: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7074 - val_loss: 0.0053 - val_acc: 0.6963 Epoch 88/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7075Epoch 00087: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7074 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 89/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7075Epoch 00088: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7074 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 90/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7064Epoch 00089: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7074 - val_loss: 0.0053 - val_acc: 0.6963 Epoch 91/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7081Epoch 00090: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7079 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 92/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7070Epoch 00091: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7074 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 93/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7087Epoch 00092: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7074 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 94/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7058Epoch 00093: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7068 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 95/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7058Epoch 00094: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7068 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 96/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7075Epoch 00095: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7074 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 97/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7075Epoch 00096: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7074 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 98/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7081Epoch 00097: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7074 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 99/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7070Epoch 00098: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7074 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 100/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7058Epoch 00099: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7068 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 101/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7070Epoch 00100: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7079 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 102/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7058Epoch 00101: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7068 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 103/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7075Epoch 00102: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7074 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 104/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7087Epoch 00103: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7074 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 105/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7087Epoch 00104: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7085 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 106/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7075Epoch 00105: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7068 - val_loss: 0.0059 - val_acc: 0.6963 Epoch 107/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7075Epoch 00106: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7074 - val_loss: 0.0051 - val_acc: 0.6963 Epoch 108/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7081Epoch 00107: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7074 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 109/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7081Epoch 00108: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7074 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 110/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7070Epoch 00109: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7074 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 111/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7093Epoch 00110: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7085 - val_loss: 0.0059 - val_acc: 0.6963 Epoch 112/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7058Epoch 00111: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7074 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 113/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7058Epoch 00112: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7062 - val_loss: 0.0059 - val_acc: 0.6963 Epoch 114/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7081Epoch 00113: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7074 - val_loss: 0.0062 - val_acc: 0.6963 Epoch 115/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7081Epoch 00114: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7079 - val_loss: 0.0062 - val_acc: 0.6963 Epoch 116/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7064Epoch 00115: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7068 - val_loss: 0.0051 - val_acc: 0.6963 Epoch 117/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7070Epoch 00116: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7074 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 118/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7075Epoch 00117: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7074 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 119/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7070Epoch 00118: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7079 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 120/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7075Epoch 00119: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7074 - val_loss: 0.0071 - val_acc: 0.6963 Epoch 121/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7058Epoch 00120: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7068 - val_loss: 0.0059 - val_acc: 0.6963 Epoch 122/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7075Epoch 00121: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7074 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 123/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7087Epoch 00122: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7074 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 124/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7075Epoch 00123: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7074 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 125/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7075Epoch 00124: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7074 - val_loss: 0.0064 - val_acc: 0.6963 Epoch 126/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7070Epoch 00125: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7074 - val_loss: 0.0061 - val_acc: 0.6963 Epoch 127/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7081Epoch 00126: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7074 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 128/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7093Epoch 00127: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7074 - val_loss: 0.0063 - val_acc: 0.6963 Epoch 129/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7081Epoch 00128: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7079 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 130/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7064Epoch 00129: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7068 - val_loss: 0.0059 - val_acc: 0.6963 Epoch 131/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7064Epoch 00130: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7074 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 132/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7070Epoch 00131: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7068 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 133/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7070Epoch 00132: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7074 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 134/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7052Epoch 00133: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7062 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 135/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7058Epoch 00134: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7074 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 136/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7058Epoch 00135: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7079 - val_loss: 0.0061 - val_acc: 0.6963 Epoch 137/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7064Epoch 00136: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7079 - val_loss: 0.0061 - val_acc: 0.6963 Epoch 138/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7081Epoch 00137: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7079 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 139/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7070Epoch 00138: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7068 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 140/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7081Epoch 00139: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7074 - val_loss: 0.0062 - val_acc: 0.6963 Epoch 141/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7075Epoch 00140: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7068 - val_loss: 0.0061 - val_acc: 0.6963 Epoch 142/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7070Epoch 00141: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7068 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 143/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7081Epoch 00142: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7079 - val_loss: 0.0054 - val_acc: 0.6963 Epoch 144/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7093Epoch 00143: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7085 - val_loss: 0.0063 - val_acc: 0.6963 Epoch 145/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7064Epoch 00144: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7068 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 146/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7052Epoch 00145: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7062 - val_loss: 0.0062 - val_acc: 0.6963 Epoch 147/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7070Epoch 00146: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7068 - val_loss: 0.0062 - val_acc: 0.6963 Epoch 148/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7070Epoch 00147: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7062 - val_loss: 0.0065 - val_acc: 0.6963 Epoch 149/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7070Epoch 00148: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7074 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 150/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7058Epoch 00149: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7074 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 151/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7070Epoch 00150: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7074 - val_loss: 0.0062 - val_acc: 0.6963 Epoch 152/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7093Epoch 00151: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7074 - val_loss: 0.0062 - val_acc: 0.6963 Epoch 153/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7087Epoch 00152: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7091 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 154/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7064Epoch 00153: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7068 - val_loss: 0.0059 - val_acc: 0.6963 Epoch 155/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7081Epoch 00154: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7074 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 156/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7058Epoch 00155: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7074 - val_loss: 0.0059 - val_acc: 0.6963 Epoch 157/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7058Epoch 00156: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7074 - val_loss: 0.0066 - val_acc: 0.6963 Epoch 158/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7058Epoch 00157: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7068 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 159/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7052Epoch 00158: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7056 - val_loss: 0.0053 - val_acc: 0.6963 Epoch 160/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7070Epoch 00159: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7068 - val_loss: 0.0062 - val_acc: 0.6963 Epoch 161/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7058Epoch 00160: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7068 - val_loss: 0.0062 - val_acc: 0.6963 Epoch 162/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7070Epoch 00161: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7079 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 163/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7058Epoch 00162: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7068 - val_loss: 0.0065 - val_acc: 0.6963 Epoch 164/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7070Epoch 00163: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7079 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 165/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7081Epoch 00164: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7068 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 166/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0039 - acc: 0.7058Epoch 00165: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0039 - acc: 0.7068 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 167/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7070Epoch 00166: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7068 - val_loss: 0.0061 - val_acc: 0.6963 Epoch 168/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0039 - acc: 0.7087Epoch 00167: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7068 - val_loss: 0.0065 - val_acc: 0.6963 Epoch 169/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0039 - acc: 0.7087Epoch 00168: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0039 - acc: 0.7074 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 170/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0039 - acc: 0.7064Epoch 00169: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0039 - acc: 0.7074 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 171/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0039 - acc: 0.7087Epoch 00170: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0039 - acc: 0.7079 - val_loss: 0.0065 - val_acc: 0.6963 Epoch 172/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0039 - acc: 0.7070Epoch 00171: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0039 - acc: 0.7068 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 173/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0039 - acc: 0.7075Epoch 00172: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0039 - acc: 0.7085 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 174/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0039 - acc: 0.7064Epoch 00173: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0039 - acc: 0.7068 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 175/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0039 - acc: 0.7075Epoch 00174: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0039 - acc: 0.7068 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 176/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0039 - acc: 0.7087Epoch 00175: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0039 - acc: 0.7085 - val_loss: 0.0063 - val_acc: 0.6963 Epoch 177/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0039 - acc: 0.7058Epoch 00176: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0038 - acc: 0.7068 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 178/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0038 - acc: 0.7070Epoch 00177: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0038 - acc: 0.7074 - val_loss: 0.0062 - val_acc: 0.6963 Epoch 179/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0038 - acc: 0.7058Epoch 00178: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0038 - acc: 0.7056 - val_loss: 0.0067 - val_acc: 0.6963 Epoch 180/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0038 - acc: 0.7070Epoch 00179: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0038 - acc: 0.7074 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 181/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0039 - acc: 0.7070Epoch 00180: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0038 - acc: 0.7068 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 182/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0038 - acc: 0.7064Epoch 00181: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0038 - acc: 0.7074 - val_loss: 0.0059 - val_acc: 0.6963 Epoch 183/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0038 - acc: 0.7070Epoch 00182: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0038 - acc: 0.7068 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 184/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0038 - acc: 0.7070Epoch 00183: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0038 - acc: 0.7068 - val_loss: 0.0063 - val_acc: 0.6963 Epoch 185/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0038 - acc: 0.7064Epoch 00184: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0038 - acc: 0.7068 - val_loss: 0.0054 - val_acc: 0.6963 Epoch 186/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0038 - acc: 0.7081Epoch 00185: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0038 - acc: 0.7062 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 187/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0038 - acc: 0.7070Epoch 00186: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0038 - acc: 0.7068 - val_loss: 0.0061 - val_acc: 0.6963 Epoch 188/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0037 - acc: 0.7070Epoch 00187: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0037 - acc: 0.7074 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 189/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0038 - acc: 0.7075Epoch 00188: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0038 - acc: 0.7068 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 190/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0037 - acc: 0.7070Epoch 00189: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0037 - acc: 0.7062 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 191/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0037 - acc: 0.7081Epoch 00190: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0037 - acc: 0.7074 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 192/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0038 - acc: 0.7081Epoch 00191: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0037 - acc: 0.7079 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 193/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0037 - acc: 0.7081Epoch 00192: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0037 - acc: 0.7085 - val_loss: 0.0061 - val_acc: 0.6963 Epoch 194/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0036 - acc: 0.7099Epoch 00193: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0037 - acc: 0.7079 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 195/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0037 - acc: 0.7087Epoch 00194: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0037 - acc: 0.7079 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 196/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0037 - acc: 0.7081Epoch 00195: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0037 - acc: 0.7068 - val_loss: 0.0062 - val_acc: 0.6963 Epoch 197/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0036 - acc: 0.7087Epoch 00196: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0036 - acc: 0.7074 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 198/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0036 - acc: 0.7087Epoch 00197: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0036 - acc: 0.7091 - val_loss: 0.0051 - val_acc: 0.6963 Epoch 199/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0036 - acc: 0.7052Epoch 00198: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0036 - acc: 0.7044 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 200/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0036 - acc: 0.7087Epoch 00199: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0036 - acc: 0.7074 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 201/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0036 - acc: 0.7093Epoch 00200: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0036 - acc: 0.7074 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 202/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0036 - acc: 0.7099Epoch 00201: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0036 - acc: 0.7085 - val_loss: 0.0059 - val_acc: 0.6963 Epoch 203/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0036 - acc: 0.7087Epoch 00202: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0036 - acc: 0.7085 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 204/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0036 - acc: 0.7105Epoch 00203: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0036 - acc: 0.7097 - val_loss: 0.0051 - val_acc: 0.6963 Epoch 205/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0036 - acc: 0.7075Epoch 00204: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0035 - acc: 0.7068 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 206/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0036 - acc: 0.7087Epoch 00205: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0036 - acc: 0.7091 - val_loss: 0.0053 - val_acc: 0.6963 Epoch 207/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0035 - acc: 0.7046Epoch 00206: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0035 - acc: 0.7062 - val_loss: 0.0051 - val_acc: 0.6963 Epoch 208/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0035 - acc: 0.7028Epoch 00207: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0035 - acc: 0.7056 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 209/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0035 - acc: 0.7099Epoch 00208: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0035 - acc: 0.7085 - val_loss: 0.0051 - val_acc: 0.6963 Epoch 210/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0035 - acc: 0.7075Epoch 00209: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0035 - acc: 0.7079 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 211/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0035 - acc: 0.7070Epoch 00210: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0035 - acc: 0.7079 - val_loss: 0.0050 - val_acc: 0.6963 Epoch 212/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0035 - acc: 0.7070Epoch 00211: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0035 - acc: 0.7068 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 213/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0035 - acc: 0.7081Epoch 00212: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0035 - acc: 0.7079 - val_loss: 0.0054 - val_acc: 0.6963 Epoch 214/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0034 - acc: 0.7058Epoch 00213: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0034 - acc: 0.7068 - val_loss: 0.0050 - val_acc: 0.6963 Epoch 215/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0034 - acc: 0.7064Epoch 00214: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0034 - acc: 0.7079 - val_loss: 0.0050 - val_acc: 0.6963 Epoch 216/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0034 - acc: 0.7075Epoch 00215: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0034 - acc: 0.7079 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 217/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0034 - acc: 0.7075Epoch 00216: val_loss improved from 0.00492 to 0.00486, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0034 - acc: 0.7079 - val_loss: 0.0049 - val_acc: 0.6963 Epoch 218/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0034 - acc: 0.7075Epoch 00217: val_loss improved from 0.00486 to 0.00482, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0034 - acc: 0.7079 - val_loss: 0.0048 - val_acc: 0.6963 Epoch 219/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0034 - acc: 0.7070Epoch 00218: val_loss improved from 0.00482 to 0.00463, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0034 - acc: 0.7091 - val_loss: 0.0046 - val_acc: 0.6963 Epoch 220/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0034 - acc: 0.7070Epoch 00219: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0034 - acc: 0.7056 - val_loss: 0.0051 - val_acc: 0.6963 Epoch 221/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0033 - acc: 0.7099Epoch 00220: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0033 - acc: 0.7091 - val_loss: 0.0048 - val_acc: 0.6963 Epoch 222/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0034 - acc: 0.7111Epoch 00221: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0033 - acc: 0.7103 - val_loss: 0.0050 - val_acc: 0.6963 Epoch 223/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0033 - acc: 0.7075Epoch 00222: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0033 - acc: 0.7068 - val_loss: 0.0048 - val_acc: 0.6963 Epoch 224/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0034 - acc: 0.7075Epoch 00223: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0034 - acc: 0.7085 - val_loss: 0.0049 - val_acc: 0.6963 Epoch 225/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0033 - acc: 0.7052Epoch 00224: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0033 - acc: 0.7056 - val_loss: 0.0050 - val_acc: 0.6963 Epoch 226/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0033 - acc: 0.7070Epoch 00225: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0033 - acc: 0.7085 - val_loss: 0.0051 - val_acc: 0.6963 Epoch 227/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0033 - acc: 0.7099Epoch 00226: val_loss improved from 0.00463 to 0.00448, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0033 - acc: 0.7091 - val_loss: 0.0045 - val_acc: 0.6963 Epoch 228/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0033 - acc: 0.7034Epoch 00227: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0033 - acc: 0.7027 - val_loss: 0.0053 - val_acc: 0.6963 Epoch 229/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0033 - acc: 0.7093Epoch 00228: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0033 - acc: 0.7097 - val_loss: 0.0053 - val_acc: 0.6963 Epoch 230/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0033 - acc: 0.7064Epoch 00229: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0033 - acc: 0.7079 - val_loss: 0.0051 - val_acc: 0.6963 Epoch 231/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0032 - acc: 0.7123Epoch 00230: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0032 - acc: 0.7132 - val_loss: 0.0054 - val_acc: 0.6963 Epoch 232/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0032 - acc: 0.7087Epoch 00231: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0032 - acc: 0.7103 - val_loss: 0.0047 - val_acc: 0.6963 Epoch 233/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0032 - acc: 0.7017Epoch 00232: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0032 - acc: 0.7027 - val_loss: 0.0046 - val_acc: 0.6963 Epoch 234/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0032 - acc: 0.7075Epoch 00233: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0032 - acc: 0.7056 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 235/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0032 - acc: 0.7052Epoch 00234: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0032 - acc: 0.7044 - val_loss: 0.0047 - val_acc: 0.6963 Epoch 236/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0031 - acc: 0.7028Epoch 00235: val_loss improved from 0.00448 to 0.00437, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0031 - acc: 0.7033 - val_loss: 0.0044 - val_acc: 0.6963 Epoch 237/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0032 - acc: 0.7075Epoch 00236: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0032 - acc: 0.7091 - val_loss: 0.0049 - val_acc: 0.6963 Epoch 238/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0032 - acc: 0.7058Epoch 00237: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0032 - acc: 0.7079 - val_loss: 0.0045 - val_acc: 0.6963 Epoch 239/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0031 - acc: 0.7111Epoch 00238: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0031 - acc: 0.7103 - val_loss: 0.0051 - val_acc: 0.6963 Epoch 240/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0031 - acc: 0.7081Epoch 00239: val_loss improved from 0.00437 to 0.00420, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0031 - acc: 0.7097 - val_loss: 0.0042 - val_acc: 0.6963 Epoch 241/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0031 - acc: 0.7093Epoch 00240: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0031 - acc: 0.7109 - val_loss: 0.0045 - val_acc: 0.6963 Epoch 242/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0031 - acc: 0.7075Epoch 00241: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0031 - acc: 0.7091 - val_loss: 0.0048 - val_acc: 0.6963 Epoch 243/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0031 - acc: 0.7099Epoch 00242: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0031 - acc: 0.7097 - val_loss: 0.0046 - val_acc: 0.6963 Epoch 244/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0031 - acc: 0.7146Epoch 00243: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0031 - acc: 0.7138 - val_loss: 0.0044 - val_acc: 0.6963 Epoch 245/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0030 - acc: 0.7105Epoch 00244: val_loss improved from 0.00420 to 0.00410, saving model to my_model_Adadelta.h5 1712/1712 [==============================] - 3s - loss: 0.0030 - acc: 0.7097 - val_loss: 0.0041 - val_acc: 0.6963 Epoch 246/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0030 - acc: 0.7064Epoch 00245: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0030 - acc: 0.7056 - val_loss: 0.0048 - val_acc: 0.6963 Epoch 247/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0030 - acc: 0.7052Epoch 00246: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0030 - acc: 0.7050 - val_loss: 0.0045 - val_acc: 0.6963 Epoch 248/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0030 - acc: 0.7176Epoch 00247: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0030 - acc: 0.7185 - val_loss: 0.0046 - val_acc: 0.6963 Epoch 249/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0030 - acc: 0.7099Epoch 00248: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0030 - acc: 0.7103 - val_loss: 0.0044 - val_acc: 0.6963 Epoch 250/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0030 - acc: 0.7152Epoch 00249: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0030 - acc: 0.7138 - val_loss: 0.0044 - val_acc: 0.6986 Adadelta loss = 0.0043618641880767365 Train on 1712 samples, validate on 428 samples Epoch 1/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0230 - acc: 0.5118Epoch 00000: val_loss improved from inf to 0.02961, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0229 - acc: 0.5146 - val_loss: 0.0296 - val_acc: 0.6963 Epoch 2/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0067 - acc: 0.6568Epoch 00001: val_loss improved from 0.02961 to 0.02119, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0067 - acc: 0.6577 - val_loss: 0.0212 - val_acc: 0.6963 Epoch 3/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0061 - acc: 0.6562Epoch 00002: val_loss improved from 0.02119 to 0.01483, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0061 - acc: 0.6565 - val_loss: 0.0148 - val_acc: 0.6963 Epoch 4/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6816Epoch 00003: val_loss improved from 0.01483 to 0.01203, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6799 - val_loss: 0.0120 - val_acc: 0.6963 Epoch 5/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0055 - acc: 0.6828Epoch 00004: val_loss improved from 0.01203 to 0.01000, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0055 - acc: 0.6828 - val_loss: 0.0100 - val_acc: 0.6963 Epoch 6/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0054 - acc: 0.6798Epoch 00005: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0054 - acc: 0.6811 - val_loss: 0.0100 - val_acc: 0.6963 Epoch 7/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0053 - acc: 0.6863Epoch 00006: val_loss improved from 0.01000 to 0.00767, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0053 - acc: 0.6881 - val_loss: 0.0077 - val_acc: 0.6963 Epoch 8/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.6999Epoch 00007: val_loss improved from 0.00767 to 0.00729, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0052 - acc: 0.6980 - val_loss: 0.0073 - val_acc: 0.6963 Epoch 9/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.6916Epoch 00008: val_loss improved from 0.00729 to 0.00665, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.6922 - val_loss: 0.0067 - val_acc: 0.6963 Epoch 10/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.6975Epoch 00009: val_loss improved from 0.00665 to 0.00597, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.6992 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 11/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7058Epoch 00010: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7050 - val_loss: 0.0066 - val_acc: 0.6963 Epoch 12/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.6987Epoch 00011: val_loss improved from 0.00597 to 0.00518, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7004 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 13/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7017Epoch 00012: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7015 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 14/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7040Epoch 00013: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7044 - val_loss: 0.0053 - val_acc: 0.6963 Epoch 15/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.7034Epoch 00014: val_loss improved from 0.00518 to 0.00514, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7044 - val_loss: 0.0051 - val_acc: 0.6963 Epoch 16/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7040Epoch 00015: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7050 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 17/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7034Epoch 00016: val_loss improved from 0.00514 to 0.00510, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7050 - val_loss: 0.0051 - val_acc: 0.6963 Epoch 18/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7011Epoch 00017: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7009 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 19/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7040Epoch 00018: val_loss improved from 0.00510 to 0.00489, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7033 - val_loss: 0.0049 - val_acc: 0.6963 Epoch 20/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7058Epoch 00019: val_loss improved from 0.00489 to 0.00484, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7068 - val_loss: 0.0048 - val_acc: 0.6963 Epoch 21/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7075Epoch 00020: val_loss improved from 0.00484 to 0.00454, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7062 - val_loss: 0.0045 - val_acc: 0.6963 Epoch 22/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7058Epoch 00021: val_loss improved from 0.00454 to 0.00448, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7074 - val_loss: 0.0045 - val_acc: 0.6963 Epoch 23/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7081Epoch 00022: val_loss improved from 0.00448 to 0.00399, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7091 - val_loss: 0.0040 - val_acc: 0.6963 Epoch 24/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0039 - acc: 0.7040Epoch 00023: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0039 - acc: 0.7056 - val_loss: 0.0044 - val_acc: 0.6963 Epoch 25/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0037 - acc: 0.7058Epoch 00024: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0037 - acc: 0.7050 - val_loss: 0.0041 - val_acc: 0.6963 Epoch 26/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0035 - acc: 0.7058Epoch 00025: val_loss improved from 0.00399 to 0.00356, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0034 - acc: 0.7062 - val_loss: 0.0036 - val_acc: 0.6963 Epoch 27/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0032 - acc: 0.7064Epoch 00026: val_loss improved from 0.00356 to 0.00334, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0032 - acc: 0.7079 - val_loss: 0.0033 - val_acc: 0.6963 Epoch 28/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0030 - acc: 0.7123Epoch 00027: val_loss improved from 0.00334 to 0.00269, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0030 - acc: 0.7114 - val_loss: 0.0027 - val_acc: 0.7009 Epoch 29/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0029 - acc: 0.7105Epoch 00028: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0029 - acc: 0.7109 - val_loss: 0.0027 - val_acc: 0.6963 Epoch 30/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0028 - acc: 0.7129Epoch 00029: val_loss improved from 0.00269 to 0.00269, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0028 - acc: 0.7132 - val_loss: 0.0027 - val_acc: 0.7009 Epoch 31/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0026 - acc: 0.7164Epoch 00030: val_loss improved from 0.00269 to 0.00238, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0026 - acc: 0.7167 - val_loss: 0.0024 - val_acc: 0.6986 Epoch 32/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0026 - acc: 0.7152Epoch 00031: val_loss improved from 0.00238 to 0.00236, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0026 - acc: 0.7167 - val_loss: 0.0024 - val_acc: 0.7009 Epoch 33/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0025 - acc: 0.7093Epoch 00032: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0025 - acc: 0.7103 - val_loss: 0.0024 - val_acc: 0.7103 Epoch 34/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0023 - acc: 0.7246Epoch 00033: val_loss improved from 0.00236 to 0.00220, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0023 - acc: 0.7255 - val_loss: 0.0022 - val_acc: 0.7103 Epoch 35/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0023 - acc: 0.7170Epoch 00034: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0023 - acc: 0.7167 - val_loss: 0.0022 - val_acc: 0.7079 Epoch 36/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7252Epoch 00035: val_loss improved from 0.00220 to 0.00209, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7249 - val_loss: 0.0021 - val_acc: 0.7079 Epoch 37/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7282Epoch 00036: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7284 - val_loss: 0.0021 - val_acc: 0.7079 Epoch 38/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0021 - acc: 0.7246Epoch 00037: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0021 - acc: 0.7243 - val_loss: 0.0022 - val_acc: 0.7103 Epoch 39/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0020 - acc: 0.7276Epoch 00038: val_loss improved from 0.00209 to 0.00185, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0020 - acc: 0.7284 - val_loss: 0.0019 - val_acc: 0.7056 Epoch 40/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0020 - acc: 0.7358Epoch 00039: val_loss improved from 0.00185 to 0.00182, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0020 - acc: 0.7366 - val_loss: 0.0018 - val_acc: 0.7150 Epoch 41/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0020 - acc: 0.7335Epoch 00040: val_loss improved from 0.00182 to 0.00172, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0020 - acc: 0.7325 - val_loss: 0.0017 - val_acc: 0.7290 Epoch 42/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0019 - acc: 0.7335Epoch 00041: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0019 - acc: 0.7331 - val_loss: 0.0020 - val_acc: 0.7126 Epoch 43/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0018 - acc: 0.7500Epoch 00042: val_loss improved from 0.00172 to 0.00166, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0018 - acc: 0.7494 - val_loss: 0.0017 - val_acc: 0.7220 Epoch 44/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0018 - acc: 0.7353Epoch 00043: val_loss improved from 0.00166 to 0.00160, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0018 - acc: 0.7325 - val_loss: 0.0016 - val_acc: 0.7407 Epoch 45/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0018 - acc: 0.7435Epoch 00044: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0018 - acc: 0.7430 - val_loss: 0.0017 - val_acc: 0.7196 Epoch 46/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0017 - acc: 0.7465Epoch 00045: val_loss improved from 0.00160 to 0.00160, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0017 - acc: 0.7477 - val_loss: 0.0016 - val_acc: 0.7407 Epoch 47/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0017 - acc: 0.7471Epoch 00046: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0017 - acc: 0.7465 - val_loss: 0.0016 - val_acc: 0.7220 Epoch 48/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0017 - acc: 0.7642Epoch 00047: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0017 - acc: 0.7634 - val_loss: 0.0017 - val_acc: 0.7173 Epoch 49/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0017 - acc: 0.7565Epoch 00048: val_loss improved from 0.00160 to 0.00154, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0016 - acc: 0.7582 - val_loss: 0.0015 - val_acc: 0.7290 Epoch 50/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0016 - acc: 0.7577Epoch 00049: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0016 - acc: 0.7593 - val_loss: 0.0017 - val_acc: 0.7266 Epoch 51/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0016 - acc: 0.7441Epoch 00050: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0016 - acc: 0.7459 - val_loss: 0.0016 - val_acc: 0.7196 Epoch 52/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0016 - acc: 0.7506Epoch 00051: val_loss improved from 0.00154 to 0.00152, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0016 - acc: 0.7518 - val_loss: 0.0015 - val_acc: 0.7103 Epoch 53/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0015 - acc: 0.7529Epoch 00052: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0015 - acc: 0.7541 - val_loss: 0.0016 - val_acc: 0.7383 Epoch 54/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0015 - acc: 0.7500Epoch 00053: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0015 - acc: 0.7500 - val_loss: 0.0016 - val_acc: 0.7430 Epoch 55/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0015 - acc: 0.7600Epoch 00054: val_loss improved from 0.00152 to 0.00139, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0015 - acc: 0.7611 - val_loss: 0.0014 - val_acc: 0.7430 Epoch 56/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0015 - acc: 0.7606Epoch 00055: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0015 - acc: 0.7599 - val_loss: 0.0016 - val_acc: 0.7266 Epoch 57/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7801Epoch 00056: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7804 - val_loss: 0.0016 - val_acc: 0.7593 Epoch 58/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7606Epoch 00057: val_loss improved from 0.00139 to 0.00138, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7617 - val_loss: 0.0014 - val_acc: 0.7500 Epoch 59/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7594Epoch 00058: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7593 - val_loss: 0.0014 - val_acc: 0.7266 Epoch 60/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7742Epoch 00059: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7734 - val_loss: 0.0014 - val_acc: 0.7290 Epoch 61/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7671Epoch 00060: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7681 - val_loss: 0.0014 - val_acc: 0.7453 Epoch 62/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7759Epoch 00061: val_loss improved from 0.00138 to 0.00135, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7757 - val_loss: 0.0013 - val_acc: 0.7500 Epoch 63/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7671Epoch 00062: val_loss improved from 0.00135 to 0.00125, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7681 - val_loss: 0.0012 - val_acc: 0.7757 Epoch 64/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7783Epoch 00063: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7786 - val_loss: 0.0014 - val_acc: 0.7477 Epoch 65/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7700Epoch 00064: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7687 - val_loss: 0.0015 - val_acc: 0.7547 Epoch 66/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7689Epoch 00065: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7687 - val_loss: 0.0013 - val_acc: 0.7500 Epoch 67/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7848Epoch 00066: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7862 - val_loss: 0.0013 - val_acc: 0.7430 Epoch 68/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7659Epoch 00067: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7669 - val_loss: 0.0014 - val_acc: 0.7430 Epoch 69/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7936Epoch 00068: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7926 - val_loss: 0.0015 - val_acc: 0.7570 Epoch 70/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7925Epoch 00069: val_loss improved from 0.00125 to 0.00122, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7921 - val_loss: 0.0012 - val_acc: 0.7477 Epoch 71/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7830Epoch 00070: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7815 - val_loss: 0.0013 - val_acc: 0.7710 Epoch 72/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7807Epoch 00071: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7804 - val_loss: 0.0012 - val_acc: 0.7757 Epoch 73/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7759Epoch 00072: val_loss improved from 0.00122 to 0.00122, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7775 - val_loss: 0.0012 - val_acc: 0.7734 Epoch 74/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7600Epoch 00073: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7611 - val_loss: 0.0013 - val_acc: 0.7383 Epoch 75/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7789Epoch 00074: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7786 - val_loss: 0.0013 - val_acc: 0.7523 Epoch 76/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7754Epoch 00075: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7751 - val_loss: 0.0012 - val_acc: 0.7523 Epoch 77/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7907Epoch 00076: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7903 - val_loss: 0.0012 - val_acc: 0.7757 Epoch 78/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7960Epoch 00077: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7967 - val_loss: 0.0013 - val_acc: 0.7710 Epoch 79/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7942Epoch 00078: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7944 - val_loss: 0.0013 - val_acc: 0.7804 Epoch 80/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7807Epoch 00079: val_loss improved from 0.00122 to 0.00119, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7804 - val_loss: 0.0012 - val_acc: 0.7921 Epoch 81/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7854Epoch 00080: val_loss improved from 0.00119 to 0.00118, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7850 - val_loss: 0.0012 - val_acc: 0.7523 Epoch 82/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7854Epoch 00081: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7862 - val_loss: 0.0012 - val_acc: 0.7850 Epoch 83/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7913Epoch 00082: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7921 - val_loss: 0.0012 - val_acc: 0.7804 Epoch 84/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7930Epoch 00083: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7926 - val_loss: 0.0012 - val_acc: 0.7967 Epoch 85/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.8060Epoch 00084: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.8055 - val_loss: 0.0012 - val_acc: 0.7921 Epoch 86/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7901Epoch 00085: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7897 - val_loss: 0.0012 - val_acc: 0.7874 Epoch 87/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7889Epoch 00086: val_loss improved from 0.00118 to 0.00112, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7886 - val_loss: 0.0011 - val_acc: 0.7827 Epoch 88/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.8019 Epoch 00087: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.8020 - val_loss: 0.0012 - val_acc: 0.7921 Epoch 89/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.7983Epoch 00088: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.7991 - val_loss: 0.0011 - val_acc: 0.7897 Epoch 90/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.8019 Epoch 00089: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.8026 - val_loss: 0.0013 - val_acc: 0.7967 Epoch 91/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.7978Epoch 00090: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.7979 - val_loss: 0.0011 - val_acc: 0.7921 Epoch 92/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.8113Epoch 00091: val_loss improved from 0.00112 to 0.00111, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.8107 - val_loss: 0.0011 - val_acc: 0.8061 Epoch 93/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.8323e-04 - acc: 0.8031Epoch 00092: val_loss improved from 0.00111 to 0.00110, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 9.8447e-04 - acc: 0.8020 - val_loss: 0.0011 - val_acc: 0.7897 Epoch 94/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.8271e-04 - acc: 0.7966Epoch 00093: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.8033e-04 - acc: 0.7961 - val_loss: 0.0012 - val_acc: 0.7804 Epoch 95/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.3851e-04 - acc: 0.7960Epoch 00094: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.3688e-04 - acc: 0.7973 - val_loss: 0.0012 - val_acc: 0.7710 Epoch 96/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.6977e-04 - acc: 0.8019Epoch 00095: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.7314e-04 - acc: 0.8020 - val_loss: 0.0013 - val_acc: 0.8061 Epoch 97/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.8381e-04 - acc: 0.7877Epoch 00096: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.8252e-04 - acc: 0.7880 - val_loss: 0.0011 - val_acc: 0.7874 Epoch 98/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.6353e-04 - acc: 0.7936Epoch 00097: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.6707e-04 - acc: 0.7938 - val_loss: 0.0011 - val_acc: 0.7804 Epoch 99/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.2826e-04 - acc: 0.7925Epoch 00098: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.3080e-04 - acc: 0.7909 - val_loss: 0.0012 - val_acc: 0.7967 Epoch 100/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.3751e-04 - acc: 0.8013Epoch 00099: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.3587e-04 - acc: 0.8008 - val_loss: 0.0011 - val_acc: 0.7967 Epoch 101/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.1597e-04 - acc: 0.8042Epoch 00100: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.1730e-04 - acc: 0.8032 - val_loss: 0.0012 - val_acc: 0.7850 Epoch 102/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.2634e-04 - acc: 0.8160Epoch 00101: val_loss improved from 0.00110 to 0.00109, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 9.2599e-04 - acc: 0.8154 - val_loss: 0.0011 - val_acc: 0.7967 Epoch 103/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.9815e-04 - acc: 0.8054Epoch 00102: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.0120e-04 - acc: 0.8049 - val_loss: 0.0011 - val_acc: 0.7897 Epoch 104/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.1990e-04 - acc: 0.8125Epoch 00103: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.1789e-04 - acc: 0.8137 - val_loss: 0.0011 - val_acc: 0.7780 Epoch 105/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.7922e-04 - acc: 0.8160Epoch 00104: val_loss improved from 0.00109 to 0.00107, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 8.8051e-04 - acc: 0.8160 - val_loss: 0.0011 - val_acc: 0.7827 Epoch 106/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.2229e-04 - acc: 0.8119Epoch 00105: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.2262e-04 - acc: 0.8113 - val_loss: 0.0011 - val_acc: 0.7874 Epoch 107/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.8414e-04 - acc: 0.8131Epoch 00106: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.8296e-04 - acc: 0.8125 - val_loss: 0.0012 - val_acc: 0.7967 Epoch 108/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.8358e-04 - acc: 0.8054Epoch 00107: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.8325e-04 - acc: 0.8067 - val_loss: 0.0012 - val_acc: 0.8084 Epoch 109/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.8489e-04 - acc: 0.8172Epoch 00108: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.8589e-04 - acc: 0.8172 - val_loss: 0.0011 - val_acc: 0.7780 Epoch 110/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.9470e-04 - acc: 0.7989Epoch 00109: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.9259e-04 - acc: 0.7991 - val_loss: 0.0011 - val_acc: 0.7780 Epoch 111/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.7685e-04 - acc: 0.8172Epoch 00110: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.7862e-04 - acc: 0.8189 - val_loss: 0.0011 - val_acc: 0.7804 Epoch 112/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.7287e-04 - acc: 0.8072Epoch 00111: val_loss improved from 0.00107 to 0.00106, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 8.7244e-04 - acc: 0.8078 - val_loss: 0.0011 - val_acc: 0.8061 Epoch 113/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.3795e-04 - acc: 0.8166Epoch 00112: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.4058e-04 - acc: 0.8166 - val_loss: 0.0011 - val_acc: 0.8014 Epoch 114/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.5753e-04 - acc: 0.8125Epoch 00113: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.5877e-04 - acc: 0.8131 - val_loss: 0.0011 - val_acc: 0.8037 Epoch 115/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.4802e-04 - acc: 0.8131Epoch 00114: val_loss improved from 0.00106 to 0.00102, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 8.4731e-04 - acc: 0.8137 - val_loss: 0.0010 - val_acc: 0.7967 Epoch 116/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.2691e-04 - acc: 0.8255Epoch 00115: val_loss improved from 0.00102 to 0.00096, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 8.2952e-04 - acc: 0.8242 - val_loss: 9.5799e-04 - val_acc: 0.8107 Epoch 117/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.4066e-04 - acc: 0.8154Epoch 00116: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.4019e-04 - acc: 0.8160 - val_loss: 0.0011 - val_acc: 0.7687 Epoch 118/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.0554e-04 - acc: 0.8178Epoch 00117: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.0495e-04 - acc: 0.8172 - val_loss: 0.0011 - val_acc: 0.8037 Epoch 119/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.1071e-04 - acc: 0.8213Epoch 00118: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.0888e-04 - acc: 0.8207 - val_loss: 0.0011 - val_acc: 0.8014 Epoch 120/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.2323e-04 - acc: 0.8184Epoch 00119: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.2474e-04 - acc: 0.8172 - val_loss: 0.0010 - val_acc: 0.8014 Epoch 121/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.1401e-04 - acc: 0.8119Epoch 00120: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.1385e-04 - acc: 0.8131 - val_loss: 0.0011 - val_acc: 0.7944 Epoch 122/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.0365e-04 - acc: 0.8137Epoch 00121: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.0317e-04 - acc: 0.8137 - val_loss: 0.0011 - val_acc: 0.7827 Epoch 123/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.2044e-04 - acc: 0.8202Epoch 00122: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.2228e-04 - acc: 0.8189 - val_loss: 0.0011 - val_acc: 0.7850 Epoch 124/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.8387e-04 - acc: 0.8202Epoch 00123: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.8685e-04 - acc: 0.8207 - val_loss: 0.0011 - val_acc: 0.7827 Epoch 125/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.0761e-04 - acc: 0.8202Epoch 00124: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.0903e-04 - acc: 0.8213 - val_loss: 0.0011 - val_acc: 0.8037 Epoch 126/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.8165e-04 - acc: 0.8143Epoch 00125: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.8389e-04 - acc: 0.8143 - val_loss: 0.0011 - val_acc: 0.7897 Epoch 127/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.8107e-04 - acc: 0.8243Epoch 00126: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.8040e-04 - acc: 0.8248 - val_loss: 0.0011 - val_acc: 0.7874 Epoch 128/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.9604e-04 - acc: 0.8154Epoch 00127: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.9564e-04 - acc: 0.8166 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 129/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.8622e-04 - acc: 0.8196Epoch 00128: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.8601e-04 - acc: 0.8201 - val_loss: 0.0010 - val_acc: 0.8178 Epoch 130/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.7303e-04 - acc: 0.8166Epoch 00129: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.7293e-04 - acc: 0.8172 - val_loss: 0.0011 - val_acc: 0.8061 Epoch 131/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6488e-04 - acc: 0.8325Epoch 00130: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6615e-04 - acc: 0.8324 - val_loss: 0.0011 - val_acc: 0.7897 Epoch 132/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6295e-04 - acc: 0.8225Epoch 00131: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6358e-04 - acc: 0.8201 - val_loss: 9.8361e-04 - val_acc: 0.7944 Epoch 133/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.7700e-04 - acc: 0.8190Epoch 00132: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.7821e-04 - acc: 0.8195 - val_loss: 0.0011 - val_acc: 0.7967 Epoch 134/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6283e-04 - acc: 0.8261Epoch 00133: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6429e-04 - acc: 0.8254 - val_loss: 0.0011 - val_acc: 0.7991 Epoch 135/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5777e-04 - acc: 0.8196Epoch 00134: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5696e-04 - acc: 0.8207 - val_loss: 0.0011 - val_acc: 0.7967 Epoch 136/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4657e-04 - acc: 0.8243Epoch 00135: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4490e-04 - acc: 0.8248 - val_loss: 0.0011 - val_acc: 0.8107 Epoch 137/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4912e-04 - acc: 0.8143Epoch 00136: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5000e-04 - acc: 0.8131 - val_loss: 0.0011 - val_acc: 0.7827 Epoch 138/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5416e-04 - acc: 0.8261Epoch 00137: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5545e-04 - acc: 0.8236 - val_loss: 0.0011 - val_acc: 0.8084 Epoch 139/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4774e-04 - acc: 0.8255Epoch 00138: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4655e-04 - acc: 0.8242 - val_loss: 0.0010 - val_acc: 0.7967 Epoch 140/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3618e-04 - acc: 0.8119Epoch 00139: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.3512e-04 - acc: 0.8107 - val_loss: 0.0010 - val_acc: 0.8154 Epoch 141/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3585e-04 - acc: 0.8249Epoch 00140: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.3536e-04 - acc: 0.8259 - val_loss: 9.9616e-04 - val_acc: 0.8154 Epoch 142/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3969e-04 - acc: 0.8196Epoch 00141: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.3903e-04 - acc: 0.8195 - val_loss: 0.0011 - val_acc: 0.7850 Epoch 143/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.2978e-04 - acc: 0.8296Epoch 00142: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.3102e-04 - acc: 0.8265 - val_loss: 0.0012 - val_acc: 0.7897 Epoch 144/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.2022e-04 - acc: 0.8166Epoch 00143: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.1978e-04 - acc: 0.8172 - val_loss: 0.0010 - val_acc: 0.8154 Epoch 145/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.0365e-04 - acc: 0.8184Epoch 00144: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.0613e-04 - acc: 0.8195 - val_loss: 0.0010 - val_acc: 0.8061 Epoch 146/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.9165e-04 - acc: 0.8314Epoch 00145: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.9248e-04 - acc: 0.8306 - val_loss: 0.0011 - val_acc: 0.8154 Epoch 147/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1777e-04 - acc: 0.8202Epoch 00146: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.1775e-04 - acc: 0.8189 - val_loss: 0.0011 - val_acc: 0.7874 Epoch 148/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.2043e-04 - acc: 0.8213Epoch 00147: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.1963e-04 - acc: 0.8218 - val_loss: 0.0011 - val_acc: 0.7734 Epoch 149/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1932e-04 - acc: 0.8219Epoch 00148: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.1825e-04 - acc: 0.8218 - val_loss: 0.0010 - val_acc: 0.8037 Epoch 150/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.7443e-04 - acc: 0.8337Epoch 00149: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.7578e-04 - acc: 0.8341 - val_loss: 9.8774e-04 - val_acc: 0.8178 Epoch 151/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.9008e-04 - acc: 0.8178Epoch 00150: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.8893e-04 - acc: 0.8172 - val_loss: 9.7675e-04 - val_acc: 0.8131 Epoch 152/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.0833e-04 - acc: 0.8379Epoch 00151: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.0875e-04 - acc: 0.8370 - val_loss: 9.9692e-04 - val_acc: 0.8084 Epoch 153/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.0348e-04 - acc: 0.8166Epoch 00152: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.0298e-04 - acc: 0.8172 - val_loss: 0.0010 - val_acc: 0.8037 Epoch 154/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.9518e-04 - acc: 0.8314Epoch 00153: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.9500e-04 - acc: 0.8318 - val_loss: 0.0012 - val_acc: 0.7967 Epoch 155/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.8803e-04 - acc: 0.8373Epoch 00154: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.8810e-04 - acc: 0.8347 - val_loss: 9.9012e-04 - val_acc: 0.7991 Epoch 156/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.8069e-04 - acc: 0.8243Epoch 00155: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.8141e-04 - acc: 0.8248 - val_loss: 0.0010 - val_acc: 0.8037 Epoch 157/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.6472e-04 - acc: 0.8331Epoch 00156: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.6396e-04 - acc: 0.8335 - val_loss: 0.0010 - val_acc: 0.8061 Epoch 158/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.9371e-04 - acc: 0.8308Epoch 00157: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.9365e-04 - acc: 0.8312 - val_loss: 0.0010 - val_acc: 0.8061 Epoch 159/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.5864e-04 - acc: 0.8438Epoch 00158: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.5804e-04 - acc: 0.8435 - val_loss: 0.0010 - val_acc: 0.8178 Epoch 160/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.7842e-04 - acc: 0.8237Epoch 00159: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.7803e-04 - acc: 0.8230 - val_loss: 0.0010 - val_acc: 0.8154 Epoch 161/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.7607e-04 - acc: 0.8390Epoch 00160: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.7470e-04 - acc: 0.8382 - val_loss: 0.0010 - val_acc: 0.8131 Epoch 162/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.7600e-04 - acc: 0.8355Epoch 00161: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.7482e-04 - acc: 0.8353 - val_loss: 0.0010 - val_acc: 0.8294 Epoch 163/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.8148e-04 - acc: 0.8331Epoch 00162: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.8086e-04 - acc: 0.8341 - val_loss: 9.8013e-04 - val_acc: 0.8341 Epoch 164/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.8147e-04 - acc: 0.8337Epoch 00163: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.7886e-04 - acc: 0.8353 - val_loss: 0.0010 - val_acc: 0.8131 Epoch 165/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.5280e-04 - acc: 0.8384Epoch 00164: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.5543e-04 - acc: 0.8370 - val_loss: 9.9453e-04 - val_acc: 0.8154 Epoch 166/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.7041e-04 - acc: 0.8320Epoch 00165: val_loss improved from 0.00096 to 0.00094, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 6.7076e-04 - acc: 0.8312 - val_loss: 9.3756e-04 - val_acc: 0.8271 Epoch 167/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.5663e-04 - acc: 0.8219Epoch 00166: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.5649e-04 - acc: 0.8236 - val_loss: 0.0011 - val_acc: 0.8107 Epoch 168/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.7433e-04 - acc: 0.8343Epoch 00167: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.7201e-04 - acc: 0.8347 - val_loss: 0.0011 - val_acc: 0.7967 Epoch 169/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.7194e-04 - acc: 0.8290Epoch 00168: val_loss improved from 0.00094 to 0.00092, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 6.7146e-04 - acc: 0.8283 - val_loss: 9.1845e-04 - val_acc: 0.8388 Epoch 170/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.4936e-04 - acc: 0.8420Epoch 00169: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.4970e-04 - acc: 0.8423 - val_loss: 0.0010 - val_acc: 0.8224 Epoch 171/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3974e-04 - acc: 0.8479Epoch 00170: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3993e-04 - acc: 0.8487 - val_loss: 9.4339e-04 - val_acc: 0.8201 Epoch 172/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.5492e-04 - acc: 0.8208Epoch 00171: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.5451e-04 - acc: 0.8224 - val_loss: 9.6014e-04 - val_acc: 0.8271 Epoch 173/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.6104e-04 - acc: 0.8355Epoch 00172: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.6057e-04 - acc: 0.8353 - val_loss: 9.7305e-04 - val_acc: 0.8107 Epoch 174/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3275e-04 - acc: 0.8349Epoch 00173: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3191e-04 - acc: 0.8353 - val_loss: 0.0010 - val_acc: 0.8107 Epoch 175/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3563e-04 - acc: 0.8390Epoch 00174: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3670e-04 - acc: 0.8388 - val_loss: 0.0010 - val_acc: 0.8201 Epoch 176/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.2692e-04 - acc: 0.8349Epoch 00175: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3032e-04 - acc: 0.8353 - val_loss: 0.0010 - val_acc: 0.8131 Epoch 177/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.4797e-04 - acc: 0.8337Epoch 00176: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.4877e-04 - acc: 0.8341 - val_loss: 9.7291e-04 - val_acc: 0.8201 Epoch 178/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3070e-04 - acc: 0.8396Epoch 00177: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.2979e-04 - acc: 0.8405 - val_loss: 0.0010 - val_acc: 0.8178 Epoch 179/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3376e-04 - acc: 0.8414Epoch 00178: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3350e-04 - acc: 0.8411 - val_loss: 0.0010 - val_acc: 0.8178 Epoch 180/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.5035e-04 - acc: 0.8337Epoch 00179: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.4986e-04 - acc: 0.8335 - val_loss: 0.0010 - val_acc: 0.8084 Epoch 181/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3174e-04 - acc: 0.8272Epoch 00180: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3236e-04 - acc: 0.8265 - val_loss: 0.0010 - val_acc: 0.8061 Epoch 182/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1488e-04 - acc: 0.8337Epoch 00181: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1660e-04 - acc: 0.8324 - val_loss: 9.7978e-04 - val_acc: 0.8037 Epoch 183/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3819e-04 - acc: 0.8438Epoch 00182: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3726e-04 - acc: 0.8429 - val_loss: 9.7987e-04 - val_acc: 0.8154 Epoch 184/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.2901e-04 - acc: 0.8514Epoch 00183: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.2914e-04 - acc: 0.8505 - val_loss: 9.7579e-04 - val_acc: 0.8084 Epoch 185/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.2650e-04 - acc: 0.8367Epoch 00184: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.2839e-04 - acc: 0.8370 - val_loss: 0.0010 - val_acc: 0.8131 Epoch 186/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1641e-04 - acc: 0.8325Epoch 00185: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1555e-04 - acc: 0.8329 - val_loss: 0.0010 - val_acc: 0.8178 Epoch 187/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3827e-04 - acc: 0.8314Epoch 00186: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3796e-04 - acc: 0.8294 - val_loss: 0.0011 - val_acc: 0.7991 Epoch 188/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1897e-04 - acc: 0.8379Epoch 00187: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.2595e-04 - acc: 0.8364 - val_loss: 9.8501e-04 - val_acc: 0.8061 Epoch 189/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.2176e-04 - acc: 0.8325Epoch 00188: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.2295e-04 - acc: 0.8324 - val_loss: 0.0010 - val_acc: 0.8201 Epoch 190/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1842e-04 - acc: 0.8443Epoch 00189: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1786e-04 - acc: 0.8452 - val_loss: 9.7885e-04 - val_acc: 0.8154 Epoch 191/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.0455e-04 - acc: 0.8384Epoch 00190: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.0384e-04 - acc: 0.8388 - val_loss: 0.0010 - val_acc: 0.8224 Epoch 192/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3224e-04 - acc: 0.8325Epoch 00191: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3137e-04 - acc: 0.8318 - val_loss: 0.0010 - val_acc: 0.8294 Epoch 193/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1667e-04 - acc: 0.8432Epoch 00192: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1527e-04 - acc: 0.8435 - val_loss: 9.9206e-04 - val_acc: 0.8084 Epoch 194/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8961e-04 - acc: 0.8473Epoch 00193: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8968e-04 - acc: 0.8475 - val_loss: 0.0010 - val_acc: 0.8037 Epoch 195/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.0134e-04 - acc: 0.8337Epoch 00194: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.0116e-04 - acc: 0.8335 - val_loss: 9.3431e-04 - val_acc: 0.8154 Epoch 196/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1279e-04 - acc: 0.8190Epoch 00195: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1081e-04 - acc: 0.8183 - val_loss: 0.0011 - val_acc: 0.8014 Epoch 197/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9789e-04 - acc: 0.8373Epoch 00196: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9669e-04 - acc: 0.8370 - val_loss: 9.8627e-04 - val_acc: 0.8201 Epoch 198/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9506e-04 - acc: 0.8438Epoch 00197: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9531e-04 - acc: 0.8440 - val_loss: 0.0011 - val_acc: 0.8084 Epoch 199/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9498e-04 - acc: 0.8343Epoch 00198: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9427e-04 - acc: 0.8341 - val_loss: 0.0010 - val_acc: 0.8037 Epoch 200/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9999e-04 - acc: 0.8414Epoch 00199: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.0041e-04 - acc: 0.8423 - val_loss: 9.6736e-04 - val_acc: 0.8201 Epoch 201/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9570e-04 - acc: 0.8361Epoch 00200: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9645e-04 - acc: 0.8353 - val_loss: 9.2913e-04 - val_acc: 0.8294 Epoch 202/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1213e-04 - acc: 0.8426Epoch 00201: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1247e-04 - acc: 0.8411 - val_loss: 9.2098e-04 - val_acc: 0.8154 Epoch 203/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.0002e-04 - acc: 0.8432Epoch 00202: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.0210e-04 - acc: 0.8446 - val_loss: 9.7479e-04 - val_acc: 0.8224 Epoch 204/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1563e-04 - acc: 0.8373Epoch 00203: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1578e-04 - acc: 0.8388 - val_loss: 9.7419e-04 - val_acc: 0.8248 Epoch 205/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8384e-04 - acc: 0.8373Epoch 00204: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8509e-04 - acc: 0.8370 - val_loss: 9.5452e-04 - val_acc: 0.8014 Epoch 206/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9183e-04 - acc: 0.8432Epoch 00205: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9169e-04 - acc: 0.8423 - val_loss: 0.0010 - val_acc: 0.8201 Epoch 207/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9924e-04 - acc: 0.8443Epoch 00206: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9922e-04 - acc: 0.8452 - val_loss: 0.0010 - val_acc: 0.8037 Epoch 208/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.0269e-04 - acc: 0.8426Epoch 00207: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.0270e-04 - acc: 0.8429 - val_loss: 0.0010 - val_acc: 0.8154 Epoch 209/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8853e-04 - acc: 0.8426Epoch 00208: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8854e-04 - acc: 0.8429 - val_loss: 9.1922e-04 - val_acc: 0.8201 Epoch 210/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9498e-04 - acc: 0.8343Epoch 00209: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9430e-04 - acc: 0.8335 - val_loss: 9.2852e-04 - val_acc: 0.8294 Epoch 211/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8343e-04 - acc: 0.8379Epoch 00210: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8377e-04 - acc: 0.8382 - val_loss: 9.3138e-04 - val_acc: 0.8294 Epoch 212/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9269e-04 - acc: 0.8343Epoch 00211: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9258e-04 - acc: 0.8347 - val_loss: 9.9391e-04 - val_acc: 0.8201 Epoch 213/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9782e-04 - acc: 0.8390Epoch 00212: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9671e-04 - acc: 0.8388 - val_loss: 9.6281e-04 - val_acc: 0.8224 Epoch 214/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9980e-04 - acc: 0.8420Epoch 00213: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.0308e-04 - acc: 0.8417 - val_loss: 0.0011 - val_acc: 0.7991 Epoch 215/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1457e-04 - acc: 0.8290Epoch 00214: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1807e-04 - acc: 0.8289 - val_loss: 9.7783e-04 - val_acc: 0.8131 Epoch 216/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1435e-04 - acc: 0.8420Epoch 00215: val_loss improved from 0.00092 to 0.00089, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 6.1381e-04 - acc: 0.8423 - val_loss: 8.9043e-04 - val_acc: 0.8131 Epoch 217/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.7116e-04 - acc: 0.8384Epoch 00216: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.7052e-04 - acc: 0.8370 - val_loss: 0.0010 - val_acc: 0.8224 Epoch 218/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8137e-04 - acc: 0.8402Epoch 00217: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8088e-04 - acc: 0.8411 - val_loss: 0.0010 - val_acc: 0.8201 Epoch 219/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8384e-04 - acc: 0.8508Epoch 00218: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8459e-04 - acc: 0.8499 - val_loss: 9.8558e-04 - val_acc: 0.8224 Epoch 220/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9858e-04 - acc: 0.8414Epoch 00219: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9747e-04 - acc: 0.8417 - val_loss: 9.6709e-04 - val_acc: 0.8224 Epoch 221/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.7423e-04 - acc: 0.8532Epoch 00220: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.7576e-04 - acc: 0.8534 - val_loss: 9.3508e-04 - val_acc: 0.8201 Epoch 222/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9852e-04 - acc: 0.8408Epoch 00221: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9680e-04 - acc: 0.8411 - val_loss: 9.5412e-04 - val_acc: 0.8154 Epoch 223/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8587e-04 - acc: 0.8390Epoch 00222: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8510e-04 - acc: 0.8394 - val_loss: 9.7428e-04 - val_acc: 0.8131 Epoch 224/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8524e-04 - acc: 0.8420Epoch 00223: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8601e-04 - acc: 0.8423 - val_loss: 9.7999e-04 - val_acc: 0.8271 Epoch 225/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8698e-04 - acc: 0.8396Epoch 00224: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8624e-04 - acc: 0.8405 - val_loss: 9.5633e-04 - val_acc: 0.8201 Epoch 226/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.5934e-04 - acc: 0.8420Epoch 00225: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.5954e-04 - acc: 0.8429 - val_loss: 9.4128e-04 - val_acc: 0.8154 Epoch 227/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.7683e-04 - acc: 0.8420Epoch 00226: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.7650e-04 - acc: 0.8423 - val_loss: 9.0988e-04 - val_acc: 0.8131 Epoch 228/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9327e-04 - acc: 0.8432Epoch 00227: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9232e-04 - acc: 0.8446 - val_loss: 9.3244e-04 - val_acc: 0.7897 Epoch 229/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.7459e-04 - acc: 0.8491Epoch 00228: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.7374e-04 - acc: 0.8499 - val_loss: 0.0010 - val_acc: 0.8294 Epoch 230/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8138e-04 - acc: 0.8325Epoch 00229: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8103e-04 - acc: 0.8318 - val_loss: 9.3489e-04 - val_acc: 0.8084 Epoch 231/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.6447e-04 - acc: 0.8467Epoch 00230: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.6412e-04 - acc: 0.8470 - val_loss: 9.6555e-04 - val_acc: 0.7967 Epoch 232/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.6719e-04 - acc: 0.8520Epoch 00231: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.6805e-04 - acc: 0.8516 - val_loss: 8.9913e-04 - val_acc: 0.8178 Epoch 233/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9165e-04 - acc: 0.8396Epoch 00232: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9244e-04 - acc: 0.8394 - val_loss: 9.4465e-04 - val_acc: 0.8014 Epoch 234/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.7119e-04 - acc: 0.8438Epoch 00233: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.7213e-04 - acc: 0.8440 - val_loss: 0.0010 - val_acc: 0.8061 Epoch 235/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.6947e-04 - acc: 0.8561Epoch 00234: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.6966e-04 - acc: 0.8551 - val_loss: 9.2405e-04 - val_acc: 0.8107 Epoch 236/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.5737e-04 - acc: 0.8343Epoch 00235: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.5678e-04 - acc: 0.8347 - val_loss: 9.5672e-04 - val_acc: 0.8154 Epoch 237/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.4474e-04 - acc: 0.8520Epoch 00236: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.4433e-04 - acc: 0.8516 - val_loss: 9.6061e-04 - val_acc: 0.8084 Epoch 238/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.5541e-04 - acc: 0.8467Epoch 00237: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.5391e-04 - acc: 0.8470 - val_loss: 9.3460e-04 - val_acc: 0.8084 Epoch 239/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.5630e-04 - acc: 0.8567Epoch 00238: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.5742e-04 - acc: 0.8569 - val_loss: 0.0010 - val_acc: 0.8131 Epoch 240/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.5095e-04 - acc: 0.8538Epoch 00239: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.5111e-04 - acc: 0.8540 - val_loss: 9.5493e-04 - val_acc: 0.8107 Epoch 241/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.7247e-04 - acc: 0.8390Epoch 00240: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.7396e-04 - acc: 0.8394 - val_loss: 9.3438e-04 - val_acc: 0.7967 Epoch 242/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.7369e-04 - acc: 0.8485Epoch 00241: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.7335e-04 - acc: 0.8481 - val_loss: 9.4035e-04 - val_acc: 0.8037 Epoch 243/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.6026e-04 - acc: 0.8408Epoch 00242: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.5917e-04 - acc: 0.8417 - val_loss: 9.3750e-04 - val_acc: 0.8271 Epoch 244/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.6940e-04 - acc: 0.8455Epoch 00243: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.6823e-04 - acc: 0.8452 - val_loss: 9.4816e-04 - val_acc: 0.8178 Epoch 245/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.6166e-04 - acc: 0.8479Epoch 00244: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.6234e-04 - acc: 0.8481 - val_loss: 9.2262e-04 - val_acc: 0.8154 Epoch 246/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.6318e-04 - acc: 0.8538Epoch 00245: val_loss improved from 0.00089 to 0.00088, saving model to my_model_Adam.h5 1712/1712 [==============================] - 3s - loss: 5.6269e-04 - acc: 0.8540 - val_loss: 8.7813e-04 - val_acc: 0.8201 Epoch 247/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.7800e-04 - acc: 0.8408Epoch 00246: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.7802e-04 - acc: 0.8405 - val_loss: 9.5640e-04 - val_acc: 0.8154 Epoch 248/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.7405e-04 - acc: 0.8432Epoch 00247: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.7453e-04 - acc: 0.8429 - val_loss: 9.7802e-04 - val_acc: 0.8271 Epoch 249/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.5867e-04 - acc: 0.8479Epoch 00248: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.5834e-04 - acc: 0.8493 - val_loss: 9.3899e-04 - val_acc: 0.8224 Epoch 250/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.7533e-04 - acc: 0.8461Epoch 00249: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.7467e-04 - acc: 0.8458 - val_loss: 0.0010 - val_acc: 0.8107 Adam loss = 0.0010134596606089829 Train on 1712 samples, validate on 428 samples Epoch 1/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0137 - acc: 0.5619Epoch 00000: val_loss improved from inf to 0.01345, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0136 - acc: 0.5613 - val_loss: 0.0134 - val_acc: 0.6963 Epoch 2/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0063 - acc: 0.6551Epoch 00001: val_loss improved from 0.01345 to 0.01323, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0062 - acc: 0.6542 - val_loss: 0.0132 - val_acc: 0.6963 Epoch 3/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0061 - acc: 0.6663Epoch 00002: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0061 - acc: 0.6653 - val_loss: 0.0139 - val_acc: 0.6963 Epoch 4/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.6763Epoch 00003: val_loss improved from 0.01323 to 0.01078, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0058 - acc: 0.6758 - val_loss: 0.0108 - val_acc: 0.6963 Epoch 5/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0056 - acc: 0.6769Epoch 00004: val_loss improved from 0.01078 to 0.00899, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0056 - acc: 0.6770 - val_loss: 0.0090 - val_acc: 0.6963 Epoch 6/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0054 - acc: 0.6816Epoch 00005: val_loss improved from 0.00899 to 0.00819, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0054 - acc: 0.6799 - val_loss: 0.0082 - val_acc: 0.6963 Epoch 7/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0053 - acc: 0.6893Epoch 00006: val_loss improved from 0.00819 to 0.00769, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0053 - acc: 0.6898 - val_loss: 0.0077 - val_acc: 0.6963 Epoch 8/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0053 - acc: 0.6975Epoch 00007: val_loss improved from 0.00769 to 0.00734, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0053 - acc: 0.6963 - val_loss: 0.0073 - val_acc: 0.6963 Epoch 9/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0053 - acc: 0.6928Epoch 00008: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0053 - acc: 0.6916 - val_loss: 0.0075 - val_acc: 0.6963 Epoch 10/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.7005Epoch 00009: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.6986 - val_loss: 0.0075 - val_acc: 0.6963 Epoch 11/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.6822Epoch 00010: val_loss improved from 0.00734 to 0.00661, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.6817 - val_loss: 0.0066 - val_acc: 0.6963 Epoch 12/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0051 - acc: 0.6952Epoch 00011: val_loss improved from 0.00661 to 0.00597, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0051 - acc: 0.6945 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 13/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.6993Epoch 00012: val_loss improved from 0.00597 to 0.00574, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.6992 - val_loss: 0.0057 - val_acc: 0.6963 Epoch 14/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.7028Epoch 00013: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.7009 - val_loss: 0.0062 - val_acc: 0.6963 Epoch 15/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0049 - acc: 0.6952Epoch 00014: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.6968 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 16/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7034Epoch 00015: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7027 - val_loss: 0.0059 - val_acc: 0.6963 Epoch 17/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.6987Epoch 00016: val_loss improved from 0.00574 to 0.00555, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7004 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 18/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7058Epoch 00017: val_loss improved from 0.00555 to 0.00531, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7050 - val_loss: 0.0053 - val_acc: 0.6963 Epoch 19/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7052Epoch 00018: val_loss improved from 0.00531 to 0.00501, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7050 - val_loss: 0.0050 - val_acc: 0.6963 Epoch 20/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7034Epoch 00019: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7033 - val_loss: 0.0060 - val_acc: 0.6963 Epoch 21/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7028Epoch 00020: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7044 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 22/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7040Epoch 00021: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7056 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 23/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7011Epoch 00022: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7015 - val_loss: 0.0050 - val_acc: 0.6963 Epoch 24/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7058Epoch 00023: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7056 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 25/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7064Epoch 00024: val_loss improved from 0.00501 to 0.00477, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7074 - val_loss: 0.0048 - val_acc: 0.6963 Epoch 26/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7070Epoch 00025: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7062 - val_loss: 0.0048 - val_acc: 0.6963 Epoch 27/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0039 - acc: 0.6981Epoch 00026: val_loss improved from 0.00477 to 0.00449, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0039 - acc: 0.6980 - val_loss: 0.0045 - val_acc: 0.6963 Epoch 28/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0038 - acc: 0.7052Epoch 00027: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0038 - acc: 0.7044 - val_loss: 0.0047 - val_acc: 0.6963 Epoch 29/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0038 - acc: 0.7028Epoch 00028: val_loss improved from 0.00449 to 0.00402, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0039 - acc: 0.7009 - val_loss: 0.0040 - val_acc: 0.6963 Epoch 30/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0037 - acc: 0.7022Epoch 00029: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0037 - acc: 0.7044 - val_loss: 0.0041 - val_acc: 0.6963 Epoch 31/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0036 - acc: 0.7075Epoch 00030: val_loss improved from 0.00402 to 0.00373, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0036 - acc: 0.7050 - val_loss: 0.0037 - val_acc: 0.6963 Epoch 32/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0035 - acc: 0.7070Epoch 00031: val_loss improved from 0.00373 to 0.00372, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0035 - acc: 0.7074 - val_loss: 0.0037 - val_acc: 0.6963 Epoch 33/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0035 - acc: 0.7123Epoch 00032: val_loss improved from 0.00372 to 0.00368, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0035 - acc: 0.7144 - val_loss: 0.0037 - val_acc: 0.6963 Epoch 34/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0033 - acc: 0.7070Epoch 00033: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0033 - acc: 0.7068 - val_loss: 0.0039 - val_acc: 0.6963 Epoch 35/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0032 - acc: 0.7093Epoch 00034: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0032 - acc: 0.7097 - val_loss: 0.0037 - val_acc: 0.6963 Epoch 36/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0032 - acc: 0.7134Epoch 00035: val_loss improved from 0.00368 to 0.00320, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0032 - acc: 0.7138 - val_loss: 0.0032 - val_acc: 0.6963 Epoch 37/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0030 - acc: 0.7164Epoch 00036: val_loss improved from 0.00320 to 0.00318, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0030 - acc: 0.7179 - val_loss: 0.0032 - val_acc: 0.6939 Epoch 38/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0029 - acc: 0.7070Epoch 00037: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0030 - acc: 0.7062 - val_loss: 0.0035 - val_acc: 0.6963 Epoch 39/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0029 - acc: 0.7034Epoch 00038: val_loss improved from 0.00318 to 0.00300, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0029 - acc: 0.7027 - val_loss: 0.0030 - val_acc: 0.6986 Epoch 40/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0028 - acc: 0.7188Epoch 00039: val_loss improved from 0.00300 to 0.00276, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0028 - acc: 0.7179 - val_loss: 0.0028 - val_acc: 0.7033 Epoch 41/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0027 - acc: 0.7193Epoch 00040: val_loss improved from 0.00276 to 0.00274, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0027 - acc: 0.7196 - val_loss: 0.0027 - val_acc: 0.6963 Epoch 42/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0026 - acc: 0.7288Epoch 00041: val_loss improved from 0.00274 to 0.00256, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0026 - acc: 0.7307 - val_loss: 0.0026 - val_acc: 0.6986 Epoch 43/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0026 - acc: 0.7305Epoch 00042: val_loss improved from 0.00256 to 0.00244, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0026 - acc: 0.7319 - val_loss: 0.0024 - val_acc: 0.7103 Epoch 44/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0026 - acc: 0.7282Epoch 00043: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0025 - acc: 0.7284 - val_loss: 0.0025 - val_acc: 0.7103 Epoch 45/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0025 - acc: 0.7205Epoch 00044: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0025 - acc: 0.7220 - val_loss: 0.0026 - val_acc: 0.7290 Epoch 46/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0024 - acc: 0.7211Epoch 00045: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0024 - acc: 0.7214 - val_loss: 0.0025 - val_acc: 0.7079 Epoch 47/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0024 - acc: 0.7370Epoch 00046: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0024 - acc: 0.7371 - val_loss: 0.0025 - val_acc: 0.7173 Epoch 48/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0023 - acc: 0.7341Epoch 00047: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0023 - acc: 0.7360 - val_loss: 0.0024 - val_acc: 0.7150 Epoch 49/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7353Epoch 00048: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7366 - val_loss: 0.0025 - val_acc: 0.7126 Epoch 50/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7406Epoch 00049: val_loss improved from 0.00244 to 0.00239, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7401 - val_loss: 0.0024 - val_acc: 0.7150 Epoch 51/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7347Epoch 00050: val_loss improved from 0.00239 to 0.00234, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7348 - val_loss: 0.0023 - val_acc: 0.7243 Epoch 52/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0021 - acc: 0.7512Epoch 00051: val_loss improved from 0.00234 to 0.00232, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0021 - acc: 0.7523 - val_loss: 0.0023 - val_acc: 0.7173 Epoch 53/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0020 - acc: 0.7382Epoch 00052: val_loss improved from 0.00232 to 0.00211, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0020 - acc: 0.7383 - val_loss: 0.0021 - val_acc: 0.7196 Epoch 54/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0020 - acc: 0.7500Epoch 00053: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0020 - acc: 0.7477 - val_loss: 0.0022 - val_acc: 0.7173 Epoch 55/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0020 - acc: 0.7394Epoch 00054: val_loss improved from 0.00211 to 0.00211, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0020 - acc: 0.7395 - val_loss: 0.0021 - val_acc: 0.7196 Epoch 56/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0019 - acc: 0.7364Epoch 00055: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0019 - acc: 0.7371 - val_loss: 0.0021 - val_acc: 0.7290 Epoch 57/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0019 - acc: 0.7512Epoch 00056: val_loss improved from 0.00211 to 0.00211, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0019 - acc: 0.7477 - val_loss: 0.0021 - val_acc: 0.7056 Epoch 58/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0018 - acc: 0.7482Epoch 00057: val_loss improved from 0.00211 to 0.00197, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0018 - acc: 0.7494 - val_loss: 0.0020 - val_acc: 0.7103 Epoch 59/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0018 - acc: 0.7518Epoch 00058: val_loss improved from 0.00197 to 0.00192, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0018 - acc: 0.7506 - val_loss: 0.0019 - val_acc: 0.7150 Epoch 60/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0018 - acc: 0.7547Epoch 00059: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0018 - acc: 0.7547 - val_loss: 0.0020 - val_acc: 0.7243 Epoch 61/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0018 - acc: 0.7677Epoch 00060: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0018 - acc: 0.7687 - val_loss: 0.0020 - val_acc: 0.7196 Epoch 62/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0017 - acc: 0.7541Epoch 00061: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0017 - acc: 0.7558 - val_loss: 0.0019 - val_acc: 0.7079 Epoch 63/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0016 - acc: 0.7435Epoch 00062: val_loss improved from 0.00192 to 0.00191, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0016 - acc: 0.7447 - val_loss: 0.0019 - val_acc: 0.7196 Epoch 64/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0016 - acc: 0.7600Epoch 00063: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0016 - acc: 0.7593 - val_loss: 0.0020 - val_acc: 0.7220 Epoch 65/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0016 - acc: 0.7671Epoch 00064: val_loss improved from 0.00191 to 0.00183, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0016 - acc: 0.7675 - val_loss: 0.0018 - val_acc: 0.7313 Epoch 66/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0016 - acc: 0.7594Epoch 00065: val_loss improved from 0.00183 to 0.00182, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0016 - acc: 0.7593 - val_loss: 0.0018 - val_acc: 0.7290 Epoch 67/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0015 - acc: 0.7653Epoch 00066: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0015 - acc: 0.7658 - val_loss: 0.0020 - val_acc: 0.7196 Epoch 68/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0015 - acc: 0.7636Epoch 00067: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0015 - acc: 0.7634 - val_loss: 0.0019 - val_acc: 0.7266 Epoch 69/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0015 - acc: 0.7624Epoch 00068: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0015 - acc: 0.7623 - val_loss: 0.0019 - val_acc: 0.7243 Epoch 70/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0015 - acc: 0.7712Epoch 00069: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0015 - acc: 0.7710 - val_loss: 0.0018 - val_acc: 0.7266 Epoch 71/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0015 - acc: 0.7765Epoch 00070: val_loss improved from 0.00182 to 0.00175, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0015 - acc: 0.7780 - val_loss: 0.0017 - val_acc: 0.7360 Epoch 72/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7783Epoch 00071: val_loss improved from 0.00175 to 0.00159, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7780 - val_loss: 0.0016 - val_acc: 0.7430 Epoch 73/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7765Epoch 00072: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7757 - val_loss: 0.0017 - val_acc: 0.7173 Epoch 74/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7748Epoch 00073: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7739 - val_loss: 0.0016 - val_acc: 0.7383 Epoch 75/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7695Epoch 00074: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7687 - val_loss: 0.0020 - val_acc: 0.7500 Epoch 76/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7754Epoch 00075: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7739 - val_loss: 0.0016 - val_acc: 0.7173 Epoch 77/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7765Epoch 00076: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7757 - val_loss: 0.0016 - val_acc: 0.7383 Epoch 78/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7883Epoch 00077: val_loss improved from 0.00159 to 0.00158, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7886 - val_loss: 0.0016 - val_acc: 0.7173 Epoch 79/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7836Epoch 00078: val_loss improved from 0.00158 to 0.00156, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7845 - val_loss: 0.0016 - val_acc: 0.7383 Epoch 80/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7771Epoch 00079: val_loss improved from 0.00156 to 0.00154, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7786 - val_loss: 0.0015 - val_acc: 0.7266 Epoch 81/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7712Epoch 00080: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7693 - val_loss: 0.0015 - val_acc: 0.7500 Epoch 82/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7913Epoch 00081: val_loss improved from 0.00154 to 0.00147, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7909 - val_loss: 0.0015 - val_acc: 0.7664 Epoch 83/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7842Epoch 00082: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7845 - val_loss: 0.0015 - val_acc: 0.7196 Epoch 84/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7960Epoch 00083: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7950 - val_loss: 0.0017 - val_acc: 0.7617 Epoch 85/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7842Epoch 00084: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7856 - val_loss: 0.0015 - val_acc: 0.7477 Epoch 86/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7836Epoch 00085: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7839 - val_loss: 0.0015 - val_acc: 0.7780 Epoch 87/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7913Epoch 00086: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7915 - val_loss: 0.0015 - val_acc: 0.7547 Epoch 88/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7913Epoch 00087: val_loss improved from 0.00147 to 0.00142, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7915 - val_loss: 0.0014 - val_acc: 0.7477 Epoch 89/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7942Epoch 00088: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7938 - val_loss: 0.0015 - val_acc: 0.7523 Epoch 90/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.8019Epoch 00089: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.8014 - val_loss: 0.0015 - val_acc: 0.7500 Epoch 91/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.8090Epoch 00090: val_loss improved from 0.00142 to 0.00138, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.8090 - val_loss: 0.0014 - val_acc: 0.7850 Epoch 92/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.8060Epoch 00091: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.8078 - val_loss: 0.0016 - val_acc: 0.7570 Epoch 93/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7930Epoch 00092: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7921 - val_loss: 0.0014 - val_acc: 0.7664 Epoch 94/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7983Epoch 00093: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7979 - val_loss: 0.0014 - val_acc: 0.7827 Epoch 95/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7930Epoch 00094: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7944 - val_loss: 0.0015 - val_acc: 0.7687 Epoch 96/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.8013Epoch 00095: val_loss improved from 0.00138 to 0.00130, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.8008 - val_loss: 0.0013 - val_acc: 0.7757 Epoch 97/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.7995Epoch 00096: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7979 - val_loss: 0.0014 - val_acc: 0.7453 Epoch 98/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.7978Epoch 00097: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.7973 - val_loss: 0.0014 - val_acc: 0.7640 Epoch 99/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.7989Epoch 00098: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.7979 - val_loss: 0.0013 - val_acc: 0.7780 Epoch 100/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.7640e-04 - acc: 0.8113Epoch 00099: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.7612e-04 - acc: 0.8102 - val_loss: 0.0014 - val_acc: 0.7874 Epoch 101/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.8409e-04 - acc: 0.8078Epoch 00100: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.8784e-04 - acc: 0.8072 - val_loss: 0.0014 - val_acc: 0.7780 Epoch 102/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.7789e-04 - acc: 0.8060Epoch 00101: val_loss improved from 0.00130 to 0.00125, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 9.7728e-04 - acc: 0.8055 - val_loss: 0.0012 - val_acc: 0.7734 Epoch 103/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.6819e-04 - acc: 0.8143Epoch 00102: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.6831e-04 - acc: 0.8148 - val_loss: 0.0013 - val_acc: 0.7710 Epoch 104/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.5972e-04 - acc: 0.8066Epoch 00103: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.6210e-04 - acc: 0.8043 - val_loss: 0.0013 - val_acc: 0.7313 Epoch 105/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.4319e-04 - acc: 0.8202Epoch 00104: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.4498e-04 - acc: 0.8213 - val_loss: 0.0013 - val_acc: 0.7944 Epoch 106/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.5944e-04 - acc: 0.8090Epoch 00105: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.6068e-04 - acc: 0.8090 - val_loss: 0.0013 - val_acc: 0.7850 Epoch 107/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.4365e-04 - acc: 0.8160Epoch 00106: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.4241e-04 - acc: 0.8172 - val_loss: 0.0013 - val_acc: 0.7757 Epoch 108/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.3608e-04 - acc: 0.8107Epoch 00107: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.3653e-04 - acc: 0.8107 - val_loss: 0.0013 - val_acc: 0.7687 Epoch 109/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.2390e-04 - acc: 0.8137Epoch 00108: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.2143e-04 - acc: 0.8143 - val_loss: 0.0014 - val_acc: 0.7734 Epoch 110/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.3179e-04 - acc: 0.8160Epoch 00109: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.3328e-04 - acc: 0.8160 - val_loss: 0.0013 - val_acc: 0.7593 Epoch 111/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.2434e-04 - acc: 0.8131Epoch 00110: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.2582e-04 - acc: 0.8125 - val_loss: 0.0013 - val_acc: 0.7640 Epoch 112/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.0641e-04 - acc: 0.8160Epoch 00111: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.0543e-04 - acc: 0.8160 - val_loss: 0.0013 - val_acc: 0.7874 Epoch 113/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.9513e-04 - acc: 0.8166Epoch 00112: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.9596e-04 - acc: 0.8160 - val_loss: 0.0013 - val_acc: 0.8037 Epoch 114/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.8284e-04 - acc: 0.8166Epoch 00113: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.8308e-04 - acc: 0.8172 - val_loss: 0.0013 - val_acc: 0.7547 Epoch 115/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.8376e-04 - acc: 0.8119Epoch 00114: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.8449e-04 - acc: 0.8131 - val_loss: 0.0013 - val_acc: 0.7593 Epoch 116/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.7902e-04 - acc: 0.8113Epoch 00115: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.7887e-04 - acc: 0.8125 - val_loss: 0.0014 - val_acc: 0.7850 Epoch 117/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.5664e-04 - acc: 0.8137Epoch 00116: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.5739e-04 - acc: 0.8137 - val_loss: 0.0013 - val_acc: 0.7570 Epoch 118/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.5193e-04 - acc: 0.8202Epoch 00117: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.5246e-04 - acc: 0.8207 - val_loss: 0.0013 - val_acc: 0.7850 Epoch 119/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.6139e-04 - acc: 0.8190Epoch 00118: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.5907e-04 - acc: 0.8201 - val_loss: 0.0013 - val_acc: 0.7850 Epoch 120/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.4378e-04 - acc: 0.8320Epoch 00119: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.4707e-04 - acc: 0.8324 - val_loss: 0.0013 - val_acc: 0.7453 Epoch 121/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.2475e-04 - acc: 0.8208Epoch 00120: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.2398e-04 - acc: 0.8183 - val_loss: 0.0014 - val_acc: 0.7757 Epoch 122/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.4158e-04 - acc: 0.8190Epoch 00121: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.4129e-04 - acc: 0.8183 - val_loss: 0.0013 - val_acc: 0.7640 Epoch 123/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.9893e-04 - acc: 0.8143Epoch 00122: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.9952e-04 - acc: 0.8143 - val_loss: 0.0013 - val_acc: 0.7710 Epoch 124/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.2536e-04 - acc: 0.8213Epoch 00123: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.2503e-04 - acc: 0.8213 - val_loss: 0.0013 - val_acc: 0.7780 Epoch 125/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.0644e-04 - acc: 0.8261Epoch 00124: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.0855e-04 - acc: 0.8259 - val_loss: 0.0013 - val_acc: 0.7687 Epoch 126/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.2117e-04 - acc: 0.8361Epoch 00125: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.2066e-04 - acc: 0.8353 - val_loss: 0.0013 - val_acc: 0.7804 Epoch 127/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.7818e-04 - acc: 0.8343Epoch 00126: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.7653e-04 - acc: 0.8335 - val_loss: 0.0013 - val_acc: 0.7897 Epoch 128/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.0085e-04 - acc: 0.8219Epoch 00127: val_loss improved from 0.00125 to 0.00123, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 7.9982e-04 - acc: 0.8218 - val_loss: 0.0012 - val_acc: 0.7687 Epoch 129/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.8797e-04 - acc: 0.8331Epoch 00128: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.8686e-04 - acc: 0.8329 - val_loss: 0.0014 - val_acc: 0.7757 Epoch 130/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.9738e-04 - acc: 0.8096Epoch 00129: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.9737e-04 - acc: 0.8102 - val_loss: 0.0013 - val_acc: 0.7547 Epoch 131/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.8205e-04 - acc: 0.8225Epoch 00130: val_loss improved from 0.00123 to 0.00122, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 7.8307e-04 - acc: 0.8236 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 132/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.8300e-04 - acc: 0.8219Epoch 00131: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.8247e-04 - acc: 0.8218 - val_loss: 0.0013 - val_acc: 0.7710 Epoch 133/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5960e-04 - acc: 0.8213Epoch 00132: val_loss improved from 0.00122 to 0.00120, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 7.5757e-04 - acc: 0.8201 - val_loss: 0.0012 - val_acc: 0.7687 Epoch 134/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4358e-04 - acc: 0.8237Epoch 00133: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4346e-04 - acc: 0.8248 - val_loss: 0.0013 - val_acc: 0.7874 Epoch 135/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3319e-04 - acc: 0.8249Epoch 00134: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.3190e-04 - acc: 0.8265 - val_loss: 0.0012 - val_acc: 0.7687 Epoch 136/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4176e-04 - acc: 0.8390Epoch 00135: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4136e-04 - acc: 0.8405 - val_loss: 0.0013 - val_acc: 0.7991 Epoch 137/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5318e-04 - acc: 0.8379Epoch 00136: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5306e-04 - acc: 0.8382 - val_loss: 0.0012 - val_acc: 0.7827 Epoch 138/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.2880e-04 - acc: 0.8367Epoch 00137: val_loss improved from 0.00120 to 0.00119, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 7.2741e-04 - acc: 0.8364 - val_loss: 0.0012 - val_acc: 0.7757 Epoch 139/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4108e-04 - acc: 0.8373Epoch 00138: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4053e-04 - acc: 0.8364 - val_loss: 0.0014 - val_acc: 0.8084 Epoch 140/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3330e-04 - acc: 0.8249Epoch 00139: val_loss improved from 0.00119 to 0.00118, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 7.3236e-04 - acc: 0.8242 - val_loss: 0.0012 - val_acc: 0.7804 Epoch 141/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.2183e-04 - acc: 0.8361Epoch 00140: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.2070e-04 - acc: 0.8364 - val_loss: 0.0012 - val_acc: 0.7967 Epoch 142/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.2555e-04 - acc: 0.8261Epoch 00141: val_loss improved from 0.00118 to 0.00118, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 7.2601e-04 - acc: 0.8265 - val_loss: 0.0012 - val_acc: 0.7547 Epoch 143/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1588e-04 - acc: 0.8320Epoch 00142: val_loss improved from 0.00118 to 0.00117, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 7.1633e-04 - acc: 0.8329 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 144/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1589e-04 - acc: 0.8438Epoch 00143: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.1433e-04 - acc: 0.8429 - val_loss: 0.0012 - val_acc: 0.7967 Epoch 145/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1413e-04 - acc: 0.8420Epoch 00144: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.1389e-04 - acc: 0.8423 - val_loss: 0.0013 - val_acc: 0.8061 Epoch 146/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.0202e-04 - acc: 0.8355Epoch 00145: val_loss improved from 0.00117 to 0.00114, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 7.0229e-04 - acc: 0.8353 - val_loss: 0.0011 - val_acc: 0.7944 Epoch 147/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.0437e-04 - acc: 0.8184Epoch 00146: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.0585e-04 - acc: 0.8183 - val_loss: 0.0012 - val_acc: 0.8131 Epoch 148/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1015e-04 - acc: 0.8426Epoch 00147: val_loss improved from 0.00114 to 0.00112, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 7.1011e-04 - acc: 0.8429 - val_loss: 0.0011 - val_acc: 0.7897 Epoch 149/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1536e-04 - acc: 0.8408Epoch 00148: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.1471e-04 - acc: 0.8400 - val_loss: 0.0012 - val_acc: 0.7757 Epoch 150/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.8759e-04 - acc: 0.8420Epoch 00149: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.8842e-04 - acc: 0.8417 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 151/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.7455e-04 - acc: 0.8408Epoch 00150: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.7420e-04 - acc: 0.8423 - val_loss: 0.0012 - val_acc: 0.7850 Epoch 152/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.6412e-04 - acc: 0.8461Epoch 00151: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.6188e-04 - acc: 0.8464 - val_loss: 0.0012 - val_acc: 0.7921 Epoch 153/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.7328e-04 - acc: 0.8261Epoch 00152: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.7165e-04 - acc: 0.8259 - val_loss: 0.0012 - val_acc: 0.7944 Epoch 154/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.5669e-04 - acc: 0.8396Epoch 00153: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.5730e-04 - acc: 0.8411 - val_loss: 0.0012 - val_acc: 0.7991 Epoch 155/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.5627e-04 - acc: 0.8496Epoch 00154: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.5675e-04 - acc: 0.8505 - val_loss: 0.0012 - val_acc: 0.8131 Epoch 156/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.5547e-04 - acc: 0.8219Epoch 00155: val_loss improved from 0.00112 to 0.00111, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 6.5501e-04 - acc: 0.8213 - val_loss: 0.0011 - val_acc: 0.8037 Epoch 157/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.5431e-04 - acc: 0.8467Epoch 00156: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.5742e-04 - acc: 0.8475 - val_loss: 0.0012 - val_acc: 0.7944 Epoch 158/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.4866e-04 - acc: 0.8320Epoch 00157: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.4808e-04 - acc: 0.8329 - val_loss: 0.0012 - val_acc: 0.7991 Epoch 159/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3859e-04 - acc: 0.8473Epoch 00158: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3890e-04 - acc: 0.8481 - val_loss: 0.0013 - val_acc: 0.7874 Epoch 160/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.4787e-04 - acc: 0.8396Epoch 00159: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.4690e-04 - acc: 0.8411 - val_loss: 0.0012 - val_acc: 0.8014 Epoch 161/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3621e-04 - acc: 0.8426Epoch 00160: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3598e-04 - acc: 0.8417 - val_loss: 0.0011 - val_acc: 0.8154 Epoch 162/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.5518e-04 - acc: 0.8455Epoch 00161: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.5550e-04 - acc: 0.8458 - val_loss: 0.0012 - val_acc: 0.8061 Epoch 163/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3197e-04 - acc: 0.8361Epoch 00162: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3083e-04 - acc: 0.8364 - val_loss: 0.0012 - val_acc: 0.7710 Epoch 164/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3134e-04 - acc: 0.8361Epoch 00163: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3064e-04 - acc: 0.8364 - val_loss: 0.0011 - val_acc: 0.8037 Epoch 165/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.2330e-04 - acc: 0.8438Epoch 00164: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.2171e-04 - acc: 0.8446 - val_loss: 0.0012 - val_acc: 0.7897 Epoch 166/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.3151e-04 - acc: 0.8384Epoch 00165: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.3191e-04 - acc: 0.8388 - val_loss: 0.0012 - val_acc: 0.8014 Epoch 167/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.2936e-04 - acc: 0.8479Epoch 00166: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.2891e-04 - acc: 0.8470 - val_loss: 0.0011 - val_acc: 0.7874 Epoch 168/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1505e-04 - acc: 0.8449Epoch 00167: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1569e-04 - acc: 0.8464 - val_loss: 0.0012 - val_acc: 0.7991 Epoch 169/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1902e-04 - acc: 0.8420Epoch 00168: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1922e-04 - acc: 0.8423 - val_loss: 0.0012 - val_acc: 0.8014 Epoch 170/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.1984e-04 - acc: 0.8438Epoch 00169: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.1879e-04 - acc: 0.8446 - val_loss: 0.0012 - val_acc: 0.7897 Epoch 171/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9185e-04 - acc: 0.8502Epoch 00170: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9219e-04 - acc: 0.8505 - val_loss: 0.0012 - val_acc: 0.7897 Epoch 172/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9944e-04 - acc: 0.8396Epoch 00171: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9823e-04 - acc: 0.8394 - val_loss: 0.0012 - val_acc: 0.7944 Epoch 173/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.0469e-04 - acc: 0.8367Epoch 00172: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.0395e-04 - acc: 0.8370 - val_loss: 0.0011 - val_acc: 0.8037 Epoch 174/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.9813e-04 - acc: 0.8438Epoch 00173: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.9784e-04 - acc: 0.8446 - val_loss: 0.0012 - val_acc: 0.8014 Epoch 175/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8570e-04 - acc: 0.8367Epoch 00174: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8486e-04 - acc: 0.8353 - val_loss: 0.0012 - val_acc: 0.7944 Epoch 176/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.7943e-04 - acc: 0.8508Epoch 00175: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8146e-04 - acc: 0.8499 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 177/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.7794e-04 - acc: 0.8485Epoch 00176: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.7684e-04 - acc: 0.8481 - val_loss: 0.0011 - val_acc: 0.7944 Epoch 178/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8058e-04 - acc: 0.8384Epoch 00177: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8051e-04 - acc: 0.8382 - val_loss: 0.0011 - val_acc: 0.7850 Epoch 179/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8514e-04 - acc: 0.8502Epoch 00178: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8479e-04 - acc: 0.8505 - val_loss: 0.0011 - val_acc: 0.7780 Epoch 180/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.8168e-04 - acc: 0.8473Epoch 00179: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.8205e-04 - acc: 0.8470 - val_loss: 0.0011 - val_acc: 0.7921 Epoch 181/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.7771e-04 - acc: 0.8508Epoch 00180: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.7689e-04 - acc: 0.8511 - val_loss: 0.0012 - val_acc: 0.7850 Epoch 182/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.7750e-04 - acc: 0.8520Epoch 00181: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.7673e-04 - acc: 0.8511 - val_loss: 0.0012 - val_acc: 0.7897 Epoch 183/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.6857e-04 - acc: 0.8538Epoch 00182: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.6888e-04 - acc: 0.8540 - val_loss: 0.0012 - val_acc: 0.7850 Epoch 184/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.7040e-04 - acc: 0.8532Epoch 00183: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.7071e-04 - acc: 0.8540 - val_loss: 0.0011 - val_acc: 0.7804 Epoch 185/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.7461e-04 - acc: 0.8491Epoch 00184: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.7359e-04 - acc: 0.8487 - val_loss: 0.0011 - val_acc: 0.7780 Epoch 186/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.6620e-04 - acc: 0.8502Epoch 00185: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.6598e-04 - acc: 0.8499 - val_loss: 0.0011 - val_acc: 0.7967 Epoch 187/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.5767e-04 - acc: 0.8485Epoch 00186: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.5791e-04 - acc: 0.8487 - val_loss: 0.0012 - val_acc: 0.7944 Epoch 188/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.6537e-04 - acc: 0.8443Epoch 00187: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.6531e-04 - acc: 0.8435 - val_loss: 0.0011 - val_acc: 0.7850 Epoch 189/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.6391e-04 - acc: 0.8514Epoch 00188: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.6350e-04 - acc: 0.8511 - val_loss: 0.0011 - val_acc: 0.7804 Epoch 190/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.5433e-04 - acc: 0.8626Epoch 00189: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.5386e-04 - acc: 0.8627 - val_loss: 0.0012 - val_acc: 0.7944 Epoch 191/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.4294e-04 - acc: 0.8396Epoch 00190: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.4421e-04 - acc: 0.8405 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 192/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.5235e-04 - acc: 0.8603Epoch 00191: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.5293e-04 - acc: 0.8598 - val_loss: 0.0012 - val_acc: 0.7944 Epoch 193/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.5153e-04 - acc: 0.8455Epoch 00192: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.5094e-04 - acc: 0.8452 - val_loss: 0.0012 - val_acc: 0.7874 Epoch 194/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.5455e-04 - acc: 0.8343Epoch 00193: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.5351e-04 - acc: 0.8347 - val_loss: 0.0011 - val_acc: 0.7921 Epoch 195/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.4435e-04 - acc: 0.8514Epoch 00194: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.4651e-04 - acc: 0.8511 - val_loss: 0.0011 - val_acc: 0.7991 Epoch 196/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.5319e-04 - acc: 0.8561Epoch 00195: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.5260e-04 - acc: 0.8557 - val_loss: 0.0011 - val_acc: 0.8061 Epoch 197/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.3373e-04 - acc: 0.8550Epoch 00196: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.3396e-04 - acc: 0.8540 - val_loss: 0.0012 - val_acc: 0.7897 Epoch 198/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.5568e-04 - acc: 0.8485Epoch 00197: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.5386e-04 - acc: 0.8475 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 199/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.2855e-04 - acc: 0.8491Epoch 00198: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.2842e-04 - acc: 0.8481 - val_loss: 0.0011 - val_acc: 0.7897 Epoch 200/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.3948e-04 - acc: 0.8550Epoch 00199: val_loss improved from 0.00111 to 0.00109, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 5.3931e-04 - acc: 0.8551 - val_loss: 0.0011 - val_acc: 0.7921 Epoch 201/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.3380e-04 - acc: 0.8597Epoch 00200: val_loss improved from 0.00109 to 0.00109, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 5.3364e-04 - acc: 0.8604 - val_loss: 0.0011 - val_acc: 0.8084 Epoch 202/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.3274e-04 - acc: 0.8491Epoch 00201: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.3405e-04 - acc: 0.8493 - val_loss: 0.0011 - val_acc: 0.7804 Epoch 203/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.1966e-04 - acc: 0.8597Epoch 00202: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.2028e-04 - acc: 0.8592 - val_loss: 0.0011 - val_acc: 0.7827 Epoch 204/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.3022e-04 - acc: 0.8567Epoch 00203: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.2951e-04 - acc: 0.8563 - val_loss: 0.0011 - val_acc: 0.8014 Epoch 205/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.1269e-04 - acc: 0.8532Epoch 00204: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.1168e-04 - acc: 0.8534 - val_loss: 0.0012 - val_acc: 0.7874 Epoch 206/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.1570e-04 - acc: 0.8555Epoch 00205: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.1626e-04 - acc: 0.8563 - val_loss: 0.0011 - val_acc: 0.7944 Epoch 207/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.1487e-04 - acc: 0.8491Epoch 00206: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.1468e-04 - acc: 0.8487 - val_loss: 0.0012 - val_acc: 0.7827 Epoch 208/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.0871e-04 - acc: 0.8638Epoch 00207: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.0890e-04 - acc: 0.8633 - val_loss: 0.0011 - val_acc: 0.7991 Epoch 209/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.0922e-04 - acc: 0.8561Epoch 00208: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.1018e-04 - acc: 0.8540 - val_loss: 0.0011 - val_acc: 0.7944 Epoch 210/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.2034e-04 - acc: 0.8585Epoch 00209: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.1966e-04 - acc: 0.8569 - val_loss: 0.0011 - val_acc: 0.7804 Epoch 211/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.1504e-04 - acc: 0.8591Epoch 00210: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.1526e-04 - acc: 0.8581 - val_loss: 0.0012 - val_acc: 0.7687 Epoch 212/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.1673e-04 - acc: 0.8579Epoch 00211: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.1829e-04 - acc: 0.8557 - val_loss: 0.0011 - val_acc: 0.7921 Epoch 213/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.1808e-04 - acc: 0.8591Epoch 00212: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.1846e-04 - acc: 0.8581 - val_loss: 0.0011 - val_acc: 0.7780 Epoch 214/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.1732e-04 - acc: 0.8555Epoch 00213: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.1657e-04 - acc: 0.8551 - val_loss: 0.0012 - val_acc: 0.7850 Epoch 215/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.9942e-04 - acc: 0.8485Epoch 00214: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.9931e-04 - acc: 0.8481 - val_loss: 0.0012 - val_acc: 0.7967 Epoch 216/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.9682e-04 - acc: 0.8538Epoch 00215: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.9698e-04 - acc: 0.8551 - val_loss: 0.0011 - val_acc: 0.7967 Epoch 217/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.0826e-04 - acc: 0.8491Epoch 00216: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.0827e-04 - acc: 0.8493 - val_loss: 0.0011 - val_acc: 0.7897 Epoch 218/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.0342e-04 - acc: 0.8514Epoch 00217: val_loss improved from 0.00109 to 0.00104, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 5.0393e-04 - acc: 0.8511 - val_loss: 0.0010 - val_acc: 0.7991 Epoch 219/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.9769e-04 - acc: 0.8544Epoch 00218: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.9841e-04 - acc: 0.8546 - val_loss: 0.0011 - val_acc: 0.8084 Epoch 220/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.8470e-04 - acc: 0.8626Epoch 00219: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.8389e-04 - acc: 0.8621 - val_loss: 0.0011 - val_acc: 0.7991 Epoch 221/250 1696/1712 [============================>.] - ETA: 0s - loss: 5.0845e-04 - acc: 0.8461Epoch 00220: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 5.0823e-04 - acc: 0.8464 - val_loss: 0.0011 - val_acc: 0.7921 Epoch 222/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.9939e-04 - acc: 0.8579Epoch 00221: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.9860e-04 - acc: 0.8557 - val_loss: 0.0010 - val_acc: 0.7991 Epoch 223/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.8175e-04 - acc: 0.8544Epoch 00222: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.8126e-04 - acc: 0.8551 - val_loss: 0.0011 - val_acc: 0.7991 Epoch 224/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.9315e-04 - acc: 0.8650Epoch 00223: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.9281e-04 - acc: 0.8633 - val_loss: 0.0011 - val_acc: 0.7944 Epoch 225/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.8757e-04 - acc: 0.8632Epoch 00224: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.8692e-04 - acc: 0.8645 - val_loss: 0.0011 - val_acc: 0.7944 Epoch 226/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.8131e-04 - acc: 0.8544Epoch 00225: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.8129e-04 - acc: 0.8557 - val_loss: 0.0012 - val_acc: 0.7991 Epoch 227/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.9082e-04 - acc: 0.8573Epoch 00226: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.9088e-04 - acc: 0.8575 - val_loss: 0.0011 - val_acc: 0.7944 Epoch 228/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.7951e-04 - acc: 0.8679Epoch 00227: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.7934e-04 - acc: 0.8680 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 229/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.8995e-04 - acc: 0.8544Epoch 00228: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.8987e-04 - acc: 0.8528 - val_loss: 0.0011 - val_acc: 0.8154 Epoch 230/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.7876e-04 - acc: 0.8614Epoch 00229: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.7779e-04 - acc: 0.8621 - val_loss: 0.0011 - val_acc: 0.8037 Epoch 231/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.8759e-04 - acc: 0.8561Epoch 00230: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.8728e-04 - acc: 0.8557 - val_loss: 0.0011 - val_acc: 0.8037 Epoch 232/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.7294e-04 - acc: 0.8502Epoch 00231: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.7357e-04 - acc: 0.8499 - val_loss: 0.0011 - val_acc: 0.8084 Epoch 233/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.8730e-04 - acc: 0.8555Epoch 00232: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.8742e-04 - acc: 0.8557 - val_loss: 0.0012 - val_acc: 0.7874 Epoch 234/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.6752e-04 - acc: 0.8709Epoch 00233: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.6715e-04 - acc: 0.8692 - val_loss: 0.0011 - val_acc: 0.8037 Epoch 235/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.9004e-04 - acc: 0.8638Epoch 00234: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.8944e-04 - acc: 0.8645 - val_loss: 0.0011 - val_acc: 0.7827 Epoch 236/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.6440e-04 - acc: 0.8567Epoch 00235: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.6553e-04 - acc: 0.8563 - val_loss: 0.0012 - val_acc: 0.7850 Epoch 237/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.7639e-04 - acc: 0.8532Epoch 00236: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.7718e-04 - acc: 0.8534 - val_loss: 0.0011 - val_acc: 0.7897 Epoch 238/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.7242e-04 - acc: 0.8703Epoch 00237: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.7182e-04 - acc: 0.8697 - val_loss: 0.0012 - val_acc: 0.7897 Epoch 239/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.6101e-04 - acc: 0.8644Epoch 00238: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.6178e-04 - acc: 0.8645 - val_loss: 0.0011 - val_acc: 0.7967 Epoch 240/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.8537e-04 - acc: 0.8644Epoch 00239: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.8486e-04 - acc: 0.8633 - val_loss: 0.0011 - val_acc: 0.8201 Epoch 241/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.7618e-04 - acc: 0.8732Epoch 00240: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.7543e-04 - acc: 0.8732 - val_loss: 0.0011 - val_acc: 0.7991 Epoch 242/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.6577e-04 - acc: 0.8673Epoch 00241: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.6673e-04 - acc: 0.8686 - val_loss: 0.0011 - val_acc: 0.7897 Epoch 243/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.7832e-04 - acc: 0.8597Epoch 00242: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.7696e-04 - acc: 0.8604 - val_loss: 0.0011 - val_acc: 0.7850 Epoch 244/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.6600e-04 - acc: 0.8679Epoch 00243: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.6722e-04 - acc: 0.8680 - val_loss: 0.0011 - val_acc: 0.7921 Epoch 245/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.7070e-04 - acc: 0.8721Epoch 00244: val_loss improved from 0.00104 to 0.00103, saving model to my_model_Adamax.h5 1712/1712 [==============================] - 3s - loss: 4.7051e-04 - acc: 0.8721 - val_loss: 0.0010 - val_acc: 0.7967 Epoch 246/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.5938e-04 - acc: 0.8632Epoch 00245: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.5946e-04 - acc: 0.8645 - val_loss: 0.0011 - val_acc: 0.7944 Epoch 247/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.6420e-04 - acc: 0.8709Epoch 00246: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.6482e-04 - acc: 0.8703 - val_loss: 0.0011 - val_acc: 0.7921 Epoch 248/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.5426e-04 - acc: 0.8691Epoch 00247: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.5297e-04 - acc: 0.8692 - val_loss: 0.0011 - val_acc: 0.7874 Epoch 249/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.5855e-04 - acc: 0.8614Epoch 00248: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.5786e-04 - acc: 0.8610 - val_loss: 0.0011 - val_acc: 0.7921 Epoch 250/250 1696/1712 [============================>.] - ETA: 0s - loss: 4.6096e-04 - acc: 0.8785Epoch 00249: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 4.6125e-04 - acc: 0.8785 - val_loss: 0.0011 - val_acc: 0.7967 Adamax loss = 0.0010946347817743772 Train on 1712 samples, validate on 428 samples Epoch 1/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0421 - acc: 0.5395Epoch 00000: val_loss improved from inf to 0.02387, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0418 - acc: 0.5380 - val_loss: 0.0239 - val_acc: 0.6963 Epoch 2/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0085 - acc: 0.6108Epoch 00001: val_loss improved from 0.02387 to 0.01127, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0086 - acc: 0.6116 - val_loss: 0.0113 - val_acc: 0.6963 Epoch 3/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0069 - acc: 0.6468Epoch 00002: val_loss improved from 0.01127 to 0.00732, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0069 - acc: 0.6449 - val_loss: 0.0073 - val_acc: 0.6963 Epoch 4/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0063 - acc: 0.6680Epoch 00003: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0063 - acc: 0.6694 - val_loss: 0.0087 - val_acc: 0.6963 Epoch 5/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0061 - acc: 0.6645Epoch 00004: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0061 - acc: 0.6665 - val_loss: 0.0076 - val_acc: 0.6963 Epoch 6/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.6904Epoch 00005: val_loss improved from 0.00732 to 0.00661, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0057 - acc: 0.6904 - val_loss: 0.0066 - val_acc: 0.6963 Epoch 7/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0054 - acc: 0.6904Epoch 00006: val_loss improved from 0.00661 to 0.00655, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0055 - acc: 0.6898 - val_loss: 0.0066 - val_acc: 0.6963 Epoch 8/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0055 - acc: 0.6869Epoch 00007: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0055 - acc: 0.6887 - val_loss: 0.0073 - val_acc: 0.6963 Epoch 9/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0053 - acc: 0.6863Epoch 00008: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0053 - acc: 0.6852 - val_loss: 0.0076 - val_acc: 0.6963 Epoch 10/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.6899Epoch 00009: val_loss improved from 0.00655 to 0.00515, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0052 - acc: 0.6904 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 11/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0052 - acc: 0.6952Epoch 00010: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0052 - acc: 0.6933 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 12/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.6993Epoch 00011: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0050 - acc: 0.6986 - val_loss: 0.0065 - val_acc: 0.6963 Epoch 13/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0050 - acc: 0.6969Epoch 00012: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0049 - acc: 0.6968 - val_loss: 0.0054 - val_acc: 0.6963 Epoch 14/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0048 - acc: 0.6940Epoch 00013: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.6951 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 15/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7017Epoch 00014: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7015 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 16/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7028Epoch 00015: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0048 - acc: 0.7033 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 17/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0047 - acc: 0.7034Epoch 00016: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0047 - acc: 0.7033 - val_loss: 0.0056 - val_acc: 0.6963 Epoch 18/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0046 - acc: 0.7081Epoch 00017: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0046 - acc: 0.7074 - val_loss: 0.0058 - val_acc: 0.6963 Epoch 19/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7046Epoch 00018: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7039 - val_loss: 0.0062 - val_acc: 0.6963 Epoch 20/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0045 - acc: 0.7005Epoch 00019: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0045 - acc: 0.7021 - val_loss: 0.0064 - val_acc: 0.6963 Epoch 21/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0044 - acc: 0.7075Epoch 00020: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0044 - acc: 0.7079 - val_loss: 0.0054 - val_acc: 0.6963 Epoch 22/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0043 - acc: 0.7022Epoch 00021: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0043 - acc: 0.7033 - val_loss: 0.0064 - val_acc: 0.6963 Epoch 23/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7052Epoch 00022: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7062 - val_loss: 0.0075 - val_acc: 0.6963 Epoch 24/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0042 - acc: 0.7017Epoch 00023: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0042 - acc: 0.7039 - val_loss: 0.0053 - val_acc: 0.6963 Epoch 25/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0041 - acc: 0.7052Epoch 00024: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0041 - acc: 0.7044 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 26/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7017Epoch 00025: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7027 - val_loss: 0.0053 - val_acc: 0.6963 Epoch 27/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0040 - acc: 0.7140Epoch 00026: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0040 - acc: 0.7132 - val_loss: 0.0054 - val_acc: 0.6963 Epoch 28/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0038 - acc: 0.7040Epoch 00027: val_loss improved from 0.00515 to 0.00502, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0038 - acc: 0.7033 - val_loss: 0.0050 - val_acc: 0.6963 Epoch 29/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0037 - acc: 0.7028Epoch 00028: val_loss improved from 0.00502 to 0.00498, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0037 - acc: 0.7015 - val_loss: 0.0050 - val_acc: 0.6963 Epoch 30/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0037 - acc: 0.7011Epoch 00029: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0037 - acc: 0.6998 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 31/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0036 - acc: 0.7093Epoch 00030: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0036 - acc: 0.7085 - val_loss: 0.0055 - val_acc: 0.6963 Epoch 32/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0036 - acc: 0.7058Epoch 00031: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0035 - acc: 0.7056 - val_loss: 0.0051 - val_acc: 0.6963 Epoch 33/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0035 - acc: 0.7099Epoch 00032: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0035 - acc: 0.7103 - val_loss: 0.0050 - val_acc: 0.6963 Epoch 34/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0035 - acc: 0.7129Epoch 00033: val_loss improved from 0.00498 to 0.00448, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0035 - acc: 0.7132 - val_loss: 0.0045 - val_acc: 0.6963 Epoch 35/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0034 - acc: 0.7140Epoch 00034: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0034 - acc: 0.7150 - val_loss: 0.0052 - val_acc: 0.6963 Epoch 36/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0032 - acc: 0.7158Epoch 00035: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0032 - acc: 0.7161 - val_loss: 0.0048 - val_acc: 0.6963 Epoch 37/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0031 - acc: 0.7170Epoch 00036: val_loss improved from 0.00448 to 0.00427, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0031 - acc: 0.7185 - val_loss: 0.0043 - val_acc: 0.6963 Epoch 38/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0029 - acc: 0.7205Epoch 00037: val_loss improved from 0.00427 to 0.00413, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0029 - acc: 0.7208 - val_loss: 0.0041 - val_acc: 0.7009 Epoch 39/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0029 - acc: 0.7288Epoch 00038: val_loss improved from 0.00413 to 0.00384, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0029 - acc: 0.7290 - val_loss: 0.0038 - val_acc: 0.6986 Epoch 40/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0027 - acc: 0.7311Epoch 00039: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0027 - acc: 0.7319 - val_loss: 0.0039 - val_acc: 0.6963 Epoch 41/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0027 - acc: 0.7258Epoch 00040: val_loss improved from 0.00384 to 0.00344, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0027 - acc: 0.7255 - val_loss: 0.0034 - val_acc: 0.7009 Epoch 42/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0025 - acc: 0.7394Epoch 00041: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0025 - acc: 0.7395 - val_loss: 0.0035 - val_acc: 0.6986 Epoch 43/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0025 - acc: 0.7347Epoch 00042: val_loss improved from 0.00344 to 0.00339, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0025 - acc: 0.7360 - val_loss: 0.0034 - val_acc: 0.7033 Epoch 44/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0024 - acc: 0.7305Epoch 00043: val_loss improved from 0.00339 to 0.00310, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0024 - acc: 0.7272 - val_loss: 0.0031 - val_acc: 0.7103 Epoch 45/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0024 - acc: 0.7311Epoch 00044: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0024 - acc: 0.7290 - val_loss: 0.0033 - val_acc: 0.7009 Epoch 46/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7524Epoch 00045: val_loss improved from 0.00310 to 0.00307, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7523 - val_loss: 0.0031 - val_acc: 0.7056 Epoch 47/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7441Epoch 00046: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7430 - val_loss: 0.0033 - val_acc: 0.7033 Epoch 48/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0022 - acc: 0.7506Epoch 00047: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0022 - acc: 0.7494 - val_loss: 0.0031 - val_acc: 0.7056 Epoch 49/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0021 - acc: 0.7447Epoch 00048: val_loss improved from 0.00307 to 0.00288, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0021 - acc: 0.7447 - val_loss: 0.0029 - val_acc: 0.7150 Epoch 50/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0021 - acc: 0.7441Epoch 00049: val_loss improved from 0.00288 to 0.00283, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0021 - acc: 0.7436 - val_loss: 0.0028 - val_acc: 0.7009 Epoch 51/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0020 - acc: 0.7506Epoch 00050: val_loss improved from 0.00283 to 0.00270, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0020 - acc: 0.7512 - val_loss: 0.0027 - val_acc: 0.7126 Epoch 52/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0020 - acc: 0.7400Epoch 00051: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0020 - acc: 0.7389 - val_loss: 0.0029 - val_acc: 0.7103 Epoch 53/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0019 - acc: 0.7565Epoch 00052: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0019 - acc: 0.7564 - val_loss: 0.0028 - val_acc: 0.7056 Epoch 54/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0019 - acc: 0.7512Epoch 00053: val_loss improved from 0.00270 to 0.00261, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0019 - acc: 0.7523 - val_loss: 0.0026 - val_acc: 0.7079 Epoch 55/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0019 - acc: 0.7700Epoch 00054: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0019 - acc: 0.7687 - val_loss: 0.0028 - val_acc: 0.7126 Epoch 56/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0018 - acc: 0.7718Epoch 00055: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0018 - acc: 0.7722 - val_loss: 0.0027 - val_acc: 0.7033 Epoch 57/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0018 - acc: 0.7541Epoch 00056: val_loss improved from 0.00261 to 0.00248, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0018 - acc: 0.7547 - val_loss: 0.0025 - val_acc: 0.7173 Epoch 58/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0018 - acc: 0.7500Epoch 00057: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0018 - acc: 0.7512 - val_loss: 0.0027 - val_acc: 0.7056 Epoch 59/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0017 - acc: 0.7535Epoch 00058: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0017 - acc: 0.7541 - val_loss: 0.0027 - val_acc: 0.7103 Epoch 60/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0016 - acc: 0.7547Epoch 00059: val_loss improved from 0.00248 to 0.00247, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0016 - acc: 0.7553 - val_loss: 0.0025 - val_acc: 0.7196 Epoch 61/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0017 - acc: 0.7624Epoch 00060: val_loss improved from 0.00247 to 0.00245, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0017 - acc: 0.7629 - val_loss: 0.0024 - val_acc: 0.7196 Epoch 62/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0016 - acc: 0.7642Epoch 00061: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0016 - acc: 0.7640 - val_loss: 0.0025 - val_acc: 0.7079 Epoch 63/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0016 - acc: 0.7571Epoch 00062: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0016 - acc: 0.7576 - val_loss: 0.0025 - val_acc: 0.7103 Epoch 64/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0016 - acc: 0.7866Epoch 00063: val_loss improved from 0.00245 to 0.00232, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0016 - acc: 0.7856 - val_loss: 0.0023 - val_acc: 0.7126 Epoch 65/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0015 - acc: 0.7700Epoch 00064: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0015 - acc: 0.7710 - val_loss: 0.0027 - val_acc: 0.7150 Epoch 66/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0015 - acc: 0.7653Epoch 00065: val_loss improved from 0.00232 to 0.00232, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0015 - acc: 0.7658 - val_loss: 0.0023 - val_acc: 0.7126 Epoch 67/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0015 - acc: 0.7706Epoch 00066: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0015 - acc: 0.7704 - val_loss: 0.0023 - val_acc: 0.7079 Epoch 68/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7807Epoch 00067: val_loss improved from 0.00232 to 0.00212, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7821 - val_loss: 0.0021 - val_acc: 0.7196 Epoch 69/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7671Epoch 00068: val_loss improved from 0.00212 to 0.00207, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7675 - val_loss: 0.0021 - val_acc: 0.7243 Epoch 70/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7736Epoch 00069: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7734 - val_loss: 0.0022 - val_acc: 0.7173 Epoch 71/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7824Epoch 00070: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7815 - val_loss: 0.0021 - val_acc: 0.7196 Epoch 72/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0014 - acc: 0.7759Epoch 00071: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0014 - acc: 0.7757 - val_loss: 0.0023 - val_acc: 0.7126 Epoch 73/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7848Epoch 00072: val_loss improved from 0.00207 to 0.00206, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7839 - val_loss: 0.0021 - val_acc: 0.7103 Epoch 74/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7824Epoch 00073: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7810 - val_loss: 0.0021 - val_acc: 0.7150 Epoch 75/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7871Epoch 00074: val_loss improved from 0.00206 to 0.00179, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7868 - val_loss: 0.0018 - val_acc: 0.7500 Epoch 76/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7925Epoch 00075: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7921 - val_loss: 0.0020 - val_acc: 0.7243 Epoch 77/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7942Epoch 00076: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7956 - val_loss: 0.0021 - val_acc: 0.7103 Epoch 78/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7925Epoch 00077: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7915 - val_loss: 0.0020 - val_acc: 0.7126 Epoch 79/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0013 - acc: 0.7966Epoch 00078: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0013 - acc: 0.7950 - val_loss: 0.0020 - val_acc: 0.7196 Epoch 80/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7901Epoch 00079: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7897 - val_loss: 0.0020 - val_acc: 0.7150 Epoch 81/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7919Epoch 00080: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7926 - val_loss: 0.0018 - val_acc: 0.7570 Epoch 82/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7925Epoch 00081: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7921 - val_loss: 0.0018 - val_acc: 0.7220 Epoch 83/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7942Epoch 00082: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7926 - val_loss: 0.0019 - val_acc: 0.7313 Epoch 84/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7989Epoch 00083: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7991 - val_loss: 0.0019 - val_acc: 0.7383 Epoch 85/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0012 - acc: 0.7877Epoch 00084: val_loss improved from 0.00179 to 0.00172, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0012 - acc: 0.7886 - val_loss: 0.0017 - val_acc: 0.7383 Epoch 86/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7877Epoch 00085: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7880 - val_loss: 0.0018 - val_acc: 0.7196 Epoch 87/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.8001Epoch 00086: val_loss improved from 0.00172 to 0.00171, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7991 - val_loss: 0.0017 - val_acc: 0.7266 Epoch 88/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.8019Epoch 00087: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.8008 - val_loss: 0.0020 - val_acc: 0.7383 Epoch 89/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7978Epoch 00088: val_loss improved from 0.00171 to 0.00165, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7979 - val_loss: 0.0016 - val_acc: 0.7453 Epoch 90/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7954Epoch 00089: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7950 - val_loss: 0.0017 - val_acc: 0.7547 Epoch 91/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.8001Epoch 00090: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.8002 - val_loss: 0.0017 - val_acc: 0.7360 Epoch 92/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7972Epoch 00091: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7973 - val_loss: 0.0017 - val_acc: 0.7383 Epoch 93/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.7960Epoch 00092: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0011 - acc: 0.7944 - val_loss: 0.0018 - val_acc: 0.7243 Epoch 94/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.7989Epoch 00093: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.7973 - val_loss: 0.0017 - val_acc: 0.7290 Epoch 95/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.8007Epoch 00094: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.8008 - val_loss: 0.0017 - val_acc: 0.7710 Epoch 96/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.8101Epoch 00095: val_loss improved from 0.00165 to 0.00165, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.8096 - val_loss: 0.0016 - val_acc: 0.7266 Epoch 97/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.7995Epoch 00096: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.8002 - val_loss: 0.0017 - val_acc: 0.7196 Epoch 98/250 1696/1712 [============================>.] - ETA: 0s - loss: 0.0010 - acc: 0.7989Epoch 00097: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 0.0010 - acc: 0.7979 - val_loss: 0.0017 - val_acc: 0.7407 Epoch 99/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.6940e-04 - acc: 0.8019Epoch 00098: val_loss improved from 0.00165 to 0.00142, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 9.7284e-04 - acc: 0.8026 - val_loss: 0.0014 - val_acc: 0.7850 Epoch 100/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.6887e-04 - acc: 0.7913Epoch 00099: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.7553e-04 - acc: 0.7909 - val_loss: 0.0016 - val_acc: 0.7383 Epoch 101/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.9872e-04 - acc: 0.8037Epoch 00100: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.9756e-04 - acc: 0.8043 - val_loss: 0.0015 - val_acc: 0.7407 Epoch 102/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.9367e-04 - acc: 0.8031Epoch 00101: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.9433e-04 - acc: 0.8020 - val_loss: 0.0014 - val_acc: 0.8061 Epoch 103/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.9955e-04 - acc: 0.8001Epoch 00102: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.9902e-04 - acc: 0.7996 - val_loss: 0.0015 - val_acc: 0.7477 Epoch 104/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.3830e-04 - acc: 0.8184Epoch 00103: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.3889e-04 - acc: 0.8172 - val_loss: 0.0015 - val_acc: 0.7570 Epoch 105/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.4439e-04 - acc: 0.8113Epoch 00104: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.4355e-04 - acc: 0.8102 - val_loss: 0.0016 - val_acc: 0.7477 Epoch 106/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.3749e-04 - acc: 0.7995Epoch 00105: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.3843e-04 - acc: 0.7996 - val_loss: 0.0016 - val_acc: 0.7547 Epoch 107/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.6282e-04 - acc: 0.8078Epoch 00106: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.6360e-04 - acc: 0.8072 - val_loss: 0.0015 - val_acc: 0.7477 Epoch 108/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.4362e-04 - acc: 0.8060Epoch 00107: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.4295e-04 - acc: 0.8072 - val_loss: 0.0016 - val_acc: 0.7710 Epoch 109/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.4389e-04 - acc: 0.8119Epoch 00108: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.4295e-04 - acc: 0.8137 - val_loss: 0.0015 - val_acc: 0.7500 Epoch 110/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.4113e-04 - acc: 0.8054Epoch 00109: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.3951e-04 - acc: 0.8067 - val_loss: 0.0015 - val_acc: 0.7757 Epoch 111/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.3111e-04 - acc: 0.8072Epoch 00110: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.2953e-04 - acc: 0.8072 - val_loss: 0.0015 - val_acc: 0.7500 Epoch 112/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.1333e-04 - acc: 0.8154Epoch 00111: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.1012e-04 - acc: 0.8166 - val_loss: 0.0014 - val_acc: 0.7664 Epoch 113/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.3000e-04 - acc: 0.8007Epoch 00112: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.2966e-04 - acc: 0.8014 - val_loss: 0.0015 - val_acc: 0.7593 Epoch 114/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.0104e-04 - acc: 0.8172Epoch 00113: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.9962e-04 - acc: 0.8189 - val_loss: 0.0015 - val_acc: 0.7313 Epoch 115/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.1333e-04 - acc: 0.8054Epoch 00114: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.1362e-04 - acc: 0.8037 - val_loss: 0.0016 - val_acc: 0.7477 Epoch 116/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.2025e-04 - acc: 0.8219Epoch 00115: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.1871e-04 - acc: 0.8218 - val_loss: 0.0014 - val_acc: 0.7617 Epoch 117/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.9998e-04 - acc: 0.8190Epoch 00116: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 9.0177e-04 - acc: 0.8189 - val_loss: 0.0014 - val_acc: 0.7780 Epoch 118/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.8066e-04 - acc: 0.8190Epoch 00117: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.8103e-04 - acc: 0.8189 - val_loss: 0.0015 - val_acc: 0.7523 Epoch 119/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.8747e-04 - acc: 0.8090Epoch 00118: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.8682e-04 - acc: 0.8090 - val_loss: 0.0015 - val_acc: 0.7617 Epoch 120/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.4493e-04 - acc: 0.8154Epoch 00119: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.4876e-04 - acc: 0.8148 - val_loss: 0.0016 - val_acc: 0.7336 Epoch 121/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.7456e-04 - acc: 0.8125Epoch 00120: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.7333e-04 - acc: 0.8102 - val_loss: 0.0016 - val_acc: 0.7360 Epoch 122/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.8191e-04 - acc: 0.8237Epoch 00121: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.8298e-04 - acc: 0.8236 - val_loss: 0.0014 - val_acc: 0.7640 Epoch 123/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.4935e-04 - acc: 0.8219Epoch 00122: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.5295e-04 - acc: 0.8207 - val_loss: 0.0016 - val_acc: 0.7477 Epoch 124/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.5052e-04 - acc: 0.8119Epoch 00123: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.4934e-04 - acc: 0.8137 - val_loss: 0.0014 - val_acc: 0.7570 Epoch 125/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.3248e-04 - acc: 0.8178Epoch 00124: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.3254e-04 - acc: 0.8154 - val_loss: 0.0016 - val_acc: 0.7687 Epoch 126/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.3819e-04 - acc: 0.8107Epoch 00125: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.3673e-04 - acc: 0.8102 - val_loss: 0.0017 - val_acc: 0.7430 Epoch 127/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.4365e-04 - acc: 0.8237Epoch 00126: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.4304e-04 - acc: 0.8242 - val_loss: 0.0014 - val_acc: 0.7430 Epoch 128/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.2826e-04 - acc: 0.8154Epoch 00127: val_loss improved from 0.00142 to 0.00135, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 8.2857e-04 - acc: 0.8154 - val_loss: 0.0013 - val_acc: 0.7500 Epoch 129/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.3387e-04 - acc: 0.8196Epoch 00128: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.3544e-04 - acc: 0.8189 - val_loss: 0.0015 - val_acc: 0.7757 Epoch 130/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.4215e-04 - acc: 0.8178Epoch 00129: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.4361e-04 - acc: 0.8148 - val_loss: 0.0015 - val_acc: 0.7734 Epoch 131/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.5430e-04 - acc: 0.8190Epoch 00130: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.5315e-04 - acc: 0.8195 - val_loss: 0.0015 - val_acc: 0.7500 Epoch 132/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.6303e-04 - acc: 0.8042Epoch 00131: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.6215e-04 - acc: 0.8037 - val_loss: 0.0015 - val_acc: 0.7757 Epoch 133/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.3700e-04 - acc: 0.8231Epoch 00132: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.3685e-04 - acc: 0.8236 - val_loss: 0.0014 - val_acc: 0.7827 Epoch 134/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.3521e-04 - acc: 0.8208Epoch 00133: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.3469e-04 - acc: 0.8213 - val_loss: 0.0016 - val_acc: 0.7430 Epoch 135/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.1806e-04 - acc: 0.8231Epoch 00134: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.1642e-04 - acc: 0.8230 - val_loss: 0.0015 - val_acc: 0.7640 Epoch 136/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.1366e-04 - acc: 0.8213Epoch 00135: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.1329e-04 - acc: 0.8218 - val_loss: 0.0014 - val_acc: 0.7593 Epoch 137/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.2928e-04 - acc: 0.8190Epoch 00136: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.3291e-04 - acc: 0.8178 - val_loss: 0.0016 - val_acc: 0.7383 Epoch 138/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.1446e-04 - acc: 0.8225Epoch 00137: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.1464e-04 - acc: 0.8218 - val_loss: 0.0016 - val_acc: 0.7523 Epoch 139/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.8405e-04 - acc: 0.8237Epoch 00138: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.9276e-04 - acc: 0.8213 - val_loss: 0.0025 - val_acc: 0.7617 Epoch 140/250 1696/1712 [============================>.] - ETA: 0s - loss: 9.8104e-04 - acc: 0.8031Epoch 00139: val_loss improved from 0.00135 to 0.00125, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 9.8388e-04 - acc: 0.8032 - val_loss: 0.0013 - val_acc: 0.7734 Epoch 141/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.7593e-04 - acc: 0.8178Epoch 00140: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.7552e-04 - acc: 0.8183 - val_loss: 0.0013 - val_acc: 0.7897 Epoch 142/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.3101e-04 - acc: 0.8172Epoch 00141: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.2985e-04 - acc: 0.8183 - val_loss: 0.0013 - val_acc: 0.7687 Epoch 143/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.1751e-04 - acc: 0.8231Epoch 00142: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.1764e-04 - acc: 0.8230 - val_loss: 0.0013 - val_acc: 0.7664 Epoch 144/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.0537e-04 - acc: 0.8172Epoch 00143: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.0410e-04 - acc: 0.8160 - val_loss: 0.0014 - val_acc: 0.7664 Epoch 145/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.8404e-04 - acc: 0.8331Epoch 00144: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.9001e-04 - acc: 0.8324 - val_loss: 0.0016 - val_acc: 0.7617 Epoch 146/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.8340e-04 - acc: 0.8213Epoch 00145: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.8495e-04 - acc: 0.8224 - val_loss: 0.0013 - val_acc: 0.7757 Epoch 147/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.2834e-04 - acc: 0.8272Epoch 00146: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.2938e-04 - acc: 0.8277 - val_loss: 0.0014 - val_acc: 0.7664 Epoch 148/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.8228e-04 - acc: 0.8196Epoch 00147: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.8135e-04 - acc: 0.8183 - val_loss: 0.0014 - val_acc: 0.7477 Epoch 149/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5841e-04 - acc: 0.8184Epoch 00148: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5679e-04 - acc: 0.8178 - val_loss: 0.0014 - val_acc: 0.7640 Epoch 150/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.9671e-04 - acc: 0.8243Epoch 00149: val_loss improved from 0.00125 to 0.00115, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 7.9755e-04 - acc: 0.8242 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 151/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.0577e-04 - acc: 0.8137Epoch 00150: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.0450e-04 - acc: 0.8148 - val_loss: 0.0015 - val_acc: 0.7453 Epoch 152/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.8883e-04 - acc: 0.8154Epoch 00151: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.8776e-04 - acc: 0.8160 - val_loss: 0.0013 - val_acc: 0.7850 Epoch 153/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6098e-04 - acc: 0.8361Epoch 00152: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6026e-04 - acc: 0.8370 - val_loss: 0.0013 - val_acc: 0.7664 Epoch 154/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.3539e-04 - acc: 0.8314Epoch 00153: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.3487e-04 - acc: 0.8318 - val_loss: 0.0012 - val_acc: 0.7734 Epoch 155/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.7914e-04 - acc: 0.8308Epoch 00154: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.7787e-04 - acc: 0.8318 - val_loss: 0.0013 - val_acc: 0.8014 Epoch 156/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.0096e-04 - acc: 0.8302Epoch 00155: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.0393e-04 - acc: 0.8289 - val_loss: 0.0014 - val_acc: 0.7593 Epoch 157/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.9646e-04 - acc: 0.8243Epoch 00156: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.9679e-04 - acc: 0.8248 - val_loss: 0.0013 - val_acc: 0.7874 Epoch 158/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.7274e-04 - acc: 0.8272Epoch 00157: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.7230e-04 - acc: 0.8265 - val_loss: 0.0014 - val_acc: 0.7547 Epoch 159/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6730e-04 - acc: 0.8284Epoch 00158: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6766e-04 - acc: 0.8294 - val_loss: 0.0013 - val_acc: 0.7687 Epoch 160/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.7074e-04 - acc: 0.8355Epoch 00159: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6993e-04 - acc: 0.8341 - val_loss: 0.0014 - val_acc: 0.7547 Epoch 161/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.7173e-04 - acc: 0.8349Epoch 00160: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.7090e-04 - acc: 0.8364 - val_loss: 0.0013 - val_acc: 0.7804 Epoch 162/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.8143e-04 - acc: 0.8261Epoch 00161: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.8093e-04 - acc: 0.8265 - val_loss: 0.0013 - val_acc: 0.7967 Epoch 163/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.7155e-04 - acc: 0.8237Epoch 00162: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.7202e-04 - acc: 0.8236 - val_loss: 0.0013 - val_acc: 0.7570 Epoch 164/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.2029e-04 - acc: 0.8225Epoch 00163: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.1772e-04 - acc: 0.8230 - val_loss: 0.0014 - val_acc: 0.7523 Epoch 165/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5979e-04 - acc: 0.8272Epoch 00164: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5783e-04 - acc: 0.8283 - val_loss: 0.0012 - val_acc: 0.7944 Epoch 166/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4446e-04 - acc: 0.8302Epoch 00165: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4320e-04 - acc: 0.8306 - val_loss: 0.0013 - val_acc: 0.7827 Epoch 167/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.8710e-04 - acc: 0.8261Epoch 00166: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.8913e-04 - acc: 0.8265 - val_loss: 0.0013 - val_acc: 0.7687 Epoch 168/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6342e-04 - acc: 0.8149Epoch 00167: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6336e-04 - acc: 0.8166 - val_loss: 0.0014 - val_acc: 0.7593 Epoch 169/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6935e-04 - acc: 0.8172Epoch 00168: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6958e-04 - acc: 0.8178 - val_loss: 0.0014 - val_acc: 0.7640 Epoch 170/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3400e-04 - acc: 0.8302Epoch 00169: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.3693e-04 - acc: 0.8312 - val_loss: 0.0014 - val_acc: 0.7687 Epoch 171/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5187e-04 - acc: 0.8249Epoch 00170: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5307e-04 - acc: 0.8254 - val_loss: 0.0012 - val_acc: 0.7874 Epoch 172/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.8266e-04 - acc: 0.8314Epoch 00171: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.8205e-04 - acc: 0.8329 - val_loss: 0.0013 - val_acc: 0.7827 Epoch 173/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6122e-04 - acc: 0.8290Epoch 00172: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6242e-04 - acc: 0.8283 - val_loss: 0.0013 - val_acc: 0.7710 Epoch 174/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.6032e-04 - acc: 0.8296Epoch 00173: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.6156e-04 - acc: 0.8294 - val_loss: 0.0013 - val_acc: 0.7804 Epoch 175/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.1803e-04 - acc: 0.8290Epoch 00174: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.1669e-04 - acc: 0.8300 - val_loss: 0.0014 - val_acc: 0.7734 Epoch 176/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.9290e-04 - acc: 0.8149Epoch 00175: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.9185e-04 - acc: 0.8154 - val_loss: 0.0014 - val_acc: 0.7850 Epoch 177/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4441e-04 - acc: 0.8267Epoch 00176: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4275e-04 - acc: 0.8259 - val_loss: 0.0012 - val_acc: 0.7804 Epoch 178/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5296e-04 - acc: 0.8379Epoch 00177: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5255e-04 - acc: 0.8376 - val_loss: 0.0013 - val_acc: 0.7850 Epoch 179/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.7285e-04 - acc: 0.8314Epoch 00178: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.7321e-04 - acc: 0.8312 - val_loss: 0.0014 - val_acc: 0.7500 Epoch 180/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4366e-04 - acc: 0.8343Epoch 00179: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4445e-04 - acc: 0.8341 - val_loss: 0.0013 - val_acc: 0.7570 Epoch 181/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6298e-04 - acc: 0.8302Epoch 00180: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6213e-04 - acc: 0.8300 - val_loss: 0.0013 - val_acc: 0.7921 Epoch 182/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6617e-04 - acc: 0.8237Epoch 00181: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6537e-04 - acc: 0.8230 - val_loss: 0.0013 - val_acc: 0.7850 Epoch 183/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6174e-04 - acc: 0.8119Epoch 00182: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6025e-04 - acc: 0.8102 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 184/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.2731e-04 - acc: 0.8243Epoch 00183: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.2887e-04 - acc: 0.8242 - val_loss: 0.0013 - val_acc: 0.7874 Epoch 185/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5353e-04 - acc: 0.8284Epoch 00184: val_loss improved from 0.00115 to 0.00111, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 7.5233e-04 - acc: 0.8294 - val_loss: 0.0011 - val_acc: 0.7827 Epoch 186/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.7581e-04 - acc: 0.8237Epoch 00185: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.7691e-04 - acc: 0.8242 - val_loss: 0.0014 - val_acc: 0.7687 Epoch 187/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4583e-04 - acc: 0.8225Epoch 00186: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4602e-04 - acc: 0.8242 - val_loss: 0.0013 - val_acc: 0.8084 Epoch 188/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4360e-04 - acc: 0.8249Epoch 00187: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4398e-04 - acc: 0.8242 - val_loss: 0.0013 - val_acc: 0.7897 Epoch 189/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5417e-04 - acc: 0.8261Epoch 00188: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5357e-04 - acc: 0.8259 - val_loss: 0.0013 - val_acc: 0.7804 Epoch 190/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6015e-04 - acc: 0.8261Epoch 00189: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5941e-04 - acc: 0.8259 - val_loss: 0.0013 - val_acc: 0.7780 Epoch 191/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.0903e-04 - acc: 0.8325Epoch 00190: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.1020e-04 - acc: 0.8335 - val_loss: 0.0013 - val_acc: 0.7664 Epoch 192/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4915e-04 - acc: 0.8284Epoch 00191: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4888e-04 - acc: 0.8294 - val_loss: 0.0012 - val_acc: 0.7804 Epoch 193/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6449e-04 - acc: 0.8237Epoch 00192: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6443e-04 - acc: 0.8236 - val_loss: 0.0013 - val_acc: 0.7850 Epoch 194/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6736e-04 - acc: 0.8190Epoch 00193: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6850e-04 - acc: 0.8195 - val_loss: 0.0012 - val_acc: 0.7874 Epoch 195/250 1696/1712 [============================>.] - ETA: 0s - loss: 8.0881e-04 - acc: 0.8272Epoch 00194: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 8.0719e-04 - acc: 0.8283 - val_loss: 0.0013 - val_acc: 0.7827 Epoch 196/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6360e-04 - acc: 0.8278Epoch 00195: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6197e-04 - acc: 0.8283 - val_loss: 0.0012 - val_acc: 0.7640 Epoch 197/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6037e-04 - acc: 0.8231Epoch 00196: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5969e-04 - acc: 0.8230 - val_loss: 0.0013 - val_acc: 0.7874 Epoch 198/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4837e-04 - acc: 0.8202Epoch 00197: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4872e-04 - acc: 0.8213 - val_loss: 0.0012 - val_acc: 0.7874 Epoch 199/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.7079e-04 - acc: 0.8143Epoch 00198: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.7393e-04 - acc: 0.8143 - val_loss: 0.0011 - val_acc: 0.7664 Epoch 200/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5283e-04 - acc: 0.8290Epoch 00199: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5150e-04 - acc: 0.8300 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 201/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5727e-04 - acc: 0.8219Epoch 00200: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5613e-04 - acc: 0.8213 - val_loss: 0.0013 - val_acc: 0.7827 Epoch 202/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5129e-04 - acc: 0.8249Epoch 00201: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5340e-04 - acc: 0.8242 - val_loss: 0.0012 - val_acc: 0.7874 Epoch 203/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6091e-04 - acc: 0.8302Epoch 00202: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6267e-04 - acc: 0.8300 - val_loss: 0.0013 - val_acc: 0.7617 Epoch 204/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5756e-04 - acc: 0.8314Epoch 00203: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5751e-04 - acc: 0.8318 - val_loss: 0.0013 - val_acc: 0.7710 Epoch 205/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6604e-04 - acc: 0.8320Epoch 00204: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6432e-04 - acc: 0.8324 - val_loss: 0.0013 - val_acc: 0.7664 Epoch 206/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6139e-04 - acc: 0.8249Epoch 00205: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6108e-04 - acc: 0.8254 - val_loss: 0.0013 - val_acc: 0.7640 Epoch 207/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4956e-04 - acc: 0.8255Epoch 00206: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4977e-04 - acc: 0.8248 - val_loss: 0.0012 - val_acc: 0.7570 Epoch 208/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3965e-04 - acc: 0.8219Epoch 00207: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4092e-04 - acc: 0.8224 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 209/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.2905e-04 - acc: 0.8320Epoch 00208: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.2814e-04 - acc: 0.8324 - val_loss: 0.0013 - val_acc: 0.7593 Epoch 210/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3336e-04 - acc: 0.8308Epoch 00209: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.3395e-04 - acc: 0.8318 - val_loss: 0.0013 - val_acc: 0.7664 Epoch 211/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.2715e-04 - acc: 0.8231Epoch 00210: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.2727e-04 - acc: 0.8236 - val_loss: 0.0012 - val_acc: 0.7850 Epoch 212/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3381e-04 - acc: 0.8284Epoch 00211: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.3513e-04 - acc: 0.8283 - val_loss: 0.0013 - val_acc: 0.7780 Epoch 213/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1401e-04 - acc: 0.8331Epoch 00212: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.1325e-04 - acc: 0.8335 - val_loss: 0.0013 - val_acc: 0.7664 Epoch 214/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3418e-04 - acc: 0.8290Epoch 00213: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.3466e-04 - acc: 0.8277 - val_loss: 0.0014 - val_acc: 0.7617 Epoch 215/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.7202e-04 - acc: 0.8325Epoch 00214: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.7074e-04 - acc: 0.8324 - val_loss: 0.0011 - val_acc: 0.7664 Epoch 216/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.2665e-04 - acc: 0.8231Epoch 00215: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.2584e-04 - acc: 0.8236 - val_loss: 0.0012 - val_acc: 0.7664 Epoch 217/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3538e-04 - acc: 0.8314Epoch 00216: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.3465e-04 - acc: 0.8318 - val_loss: 0.0012 - val_acc: 0.7593 Epoch 218/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5068e-04 - acc: 0.8302Epoch 00217: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4944e-04 - acc: 0.8300 - val_loss: 0.0012 - val_acc: 0.7687 Epoch 219/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1996e-04 - acc: 0.8355Epoch 00218: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.2083e-04 - acc: 0.8359 - val_loss: 0.0012 - val_acc: 0.7570 Epoch 220/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1542e-04 - acc: 0.8296Epoch 00219: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.1502e-04 - acc: 0.8306 - val_loss: 0.0011 - val_acc: 0.7757 Epoch 221/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1899e-04 - acc: 0.8290Epoch 00220: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.1830e-04 - acc: 0.8300 - val_loss: 0.0011 - val_acc: 0.7874 Epoch 222/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.8249e-04 - acc: 0.8373Epoch 00221: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.8466e-04 - acc: 0.8364 - val_loss: 0.0012 - val_acc: 0.7804 Epoch 223/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.2005e-04 - acc: 0.8355Epoch 00222: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.2288e-04 - acc: 0.8364 - val_loss: 0.0012 - val_acc: 0.7874 Epoch 224/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1940e-04 - acc: 0.8272Epoch 00223: val_loss improved from 0.00111 to 0.00110, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 7.1890e-04 - acc: 0.8259 - val_loss: 0.0011 - val_acc: 0.7850 Epoch 225/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3562e-04 - acc: 0.8278Epoch 00224: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.3514e-04 - acc: 0.8283 - val_loss: 0.0012 - val_acc: 0.7874 Epoch 226/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.2292e-04 - acc: 0.8308Epoch 00225: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.2386e-04 - acc: 0.8324 - val_loss: 0.0013 - val_acc: 0.7570 Epoch 227/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1044e-04 - acc: 0.8225Epoch 00226: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.1298e-04 - acc: 0.8213 - val_loss: 0.0013 - val_acc: 0.7874 Epoch 228/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.7753e-04 - acc: 0.8461Epoch 00227: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.7727e-04 - acc: 0.8470 - val_loss: 0.0014 - val_acc: 0.7617 Epoch 229/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.6014e-04 - acc: 0.8184Epoch 00228: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.6405e-04 - acc: 0.8183 - val_loss: 0.0012 - val_acc: 0.7804 Epoch 230/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.5267e-04 - acc: 0.8225Epoch 00229: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.5334e-04 - acc: 0.8230 - val_loss: 0.0012 - val_acc: 0.7850 Epoch 231/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.2345e-04 - acc: 0.8107Epoch 00230: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.2325e-04 - acc: 0.8119 - val_loss: 0.0011 - val_acc: 0.7874 Epoch 232/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.2596e-04 - acc: 0.8172Epoch 00231: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.2629e-04 - acc: 0.8172 - val_loss: 0.0013 - val_acc: 0.7874 Epoch 233/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4229e-04 - acc: 0.8331Epoch 00232: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4604e-04 - acc: 0.8324 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 234/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3729e-04 - acc: 0.8225Epoch 00233: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.3688e-04 - acc: 0.8224 - val_loss: 0.0012 - val_acc: 0.7640 Epoch 235/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.9271e-04 - acc: 0.8149Epoch 00234: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.9116e-04 - acc: 0.8143 - val_loss: 0.0013 - val_acc: 0.7710 Epoch 236/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1752e-04 - acc: 0.8320Epoch 00235: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.1786e-04 - acc: 0.8324 - val_loss: 0.0011 - val_acc: 0.7804 Epoch 237/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4945e-04 - acc: 0.8337Epoch 00236: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4662e-04 - acc: 0.8341 - val_loss: 0.0012 - val_acc: 0.8061 Epoch 238/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1989e-04 - acc: 0.8255Epoch 00237: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.1964e-04 - acc: 0.8259 - val_loss: 0.0013 - val_acc: 0.7617 Epoch 239/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.0612e-04 - acc: 0.8278Epoch 00238: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.0856e-04 - acc: 0.8289 - val_loss: 0.0012 - val_acc: 0.7734 Epoch 240/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.0603e-04 - acc: 0.8278Epoch 00239: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.0667e-04 - acc: 0.8265 - val_loss: 0.0011 - val_acc: 0.7874 Epoch 241/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.2447e-04 - acc: 0.8196Epoch 00240: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.2295e-04 - acc: 0.8195 - val_loss: 0.0012 - val_acc: 0.7617 Epoch 242/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.9849e-04 - acc: 0.8426Epoch 00241: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.9658e-04 - acc: 0.8429 - val_loss: 0.0012 - val_acc: 0.7780 Epoch 243/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.8607e-04 - acc: 0.8355Epoch 00242: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.8911e-04 - acc: 0.8353 - val_loss: 0.0011 - val_acc: 0.7921 Epoch 244/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.0663e-04 - acc: 0.8261Epoch 00243: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.0704e-04 - acc: 0.8259 - val_loss: 0.0013 - val_acc: 0.7687 Epoch 245/250 1696/1712 [============================>.] - ETA: 0s - loss: 6.9308e-04 - acc: 0.8320Epoch 00244: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 6.9265e-04 - acc: 0.8335 - val_loss: 0.0012 - val_acc: 0.7687 Epoch 246/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.3918e-04 - acc: 0.8255Epoch 00245: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.3960e-04 - acc: 0.8265 - val_loss: 0.0011 - val_acc: 0.7874 Epoch 247/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4063e-04 - acc: 0.8325Epoch 00246: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4148e-04 - acc: 0.8329 - val_loss: 0.0012 - val_acc: 0.7804 Epoch 248/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4334e-04 - acc: 0.8255Epoch 00247: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4313e-04 - acc: 0.8259 - val_loss: 0.0012 - val_acc: 0.7804 Epoch 249/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.1284e-04 - acc: 0.8379Epoch 00248: val_loss improved from 0.00110 to 0.00107, saving model to my_model_Nadam.h5 1712/1712 [==============================] - 3s - loss: 7.1340e-04 - acc: 0.8364 - val_loss: 0.0011 - val_acc: 0.7710 Epoch 250/250 1696/1712 [============================>.] - ETA: 0s - loss: 7.4683e-04 - acc: 0.8349Epoch 00249: val_loss did not improve 1712/1712 [==============================] - 3s - loss: 7.4818e-04 - acc: 0.8329 - val_loss: 0.0013 - val_acc: 0.7687 Nadam loss = 0.0013175898222862004
(IMPLEMENTATION) Answer a few questions and visualize the loss¶
Question 1: Outline the steps you took to get to your final neural network architecture and your reasoning at each step.
Answer: I used the well known technique for image convolutions to reduce size and increase depth. This was achived through several conv2D layers followed with dropuout layers with probabilities that were modified several times to find the right value. A pooling layer is added between each convolution stage to help fight overfitting and it appears it did a good job. I was getting good-enough results and I remembered form the lessons the revolutionare GAP layer so I tried adding it instead of a plain Flatten layer at the end of the network and it gave me good reults. I changed form padding=same to padding=valid on the first layers and that gave the NN a performance boost, allowing me to breach the .001 loss milestone. I am still a newbie on the subject of neural nets architectures but this project helped me start to build an intuition as to which layers are needed and why my model is performing in a given way.
Question 2: Defend your choice of optimizer. Which optimizers did you test, and how did you determine which worked best?
Answer: I tried all of them and saved their training history, you can see the plots and best losses below for each one. The best one was Adam optimizer.
Use the code cell below to plot the training and validation loss of your neural network. You may find this resource useful.
In [6]:
## TODO: Visualize the training and validation loss of your neural network
for k,v in results.items():
print('{} loss: {}'.format(k, str(min(v.history['val_loss']))))
plt.plot(v.history['acc'])
plt.plot(v.history['val_acc'])
plt.title('Model Accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(v.history['loss'])
plt.plot(v.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
print('-------------------------------------------------')
hist_Adadelta loss: 0.00410199385119
------------------------------------------------- hist_Adagrad loss: 0.0020609607215
------------------------------------------------- hist_Adamax loss: 0.00102729728515
------------------------------------------------- hist_RMSprop loss: 0.000996152834453
------------------------------------------------- hist_Adam loss: 0.000878128952077
------------------------------------------------- hist_SGD loss: 0.0200344918418
------------------------------------------------- hist_Nadam loss: 0.00106636798945
-------------------------------------------------
Question 3: Do you notice any evidence of overfitting or underfitting in the above plot? If so, what steps have you taken to improve your model? Note that slight overfitting or underfitting will not hurt your chances of a successful submission, as long as you have attempted some solutions towards improving your model (such as regularization, dropout, increased/decreased number of layers, etc).¶
Answer: There are small signs of overfitting with most of the optimizers I tried. It was worse before I removed some layers and tinkered with the droput probability to try to solve it. I had several layers stacked up and I figured simplifying the model would help both training time and fitting. Maybe 250 epochs were too many but it does not look like its too serious.
Visualize a Subset of the Test Predictions¶
Execute the code cell below to visualize your model's predicted keypoints on a subset of the testing images.
In [11]:
y_test = model.predict(X_test)
fig = plt.figure(figsize=(20,20))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
for i in range(9):
ax = fig.add_subplot(3, 3, i + 1, xticks=[], yticks=[])
plot_data(X_test[i], y_test[i], ax)
Step 8: Complete the pipeline¶
With the work you did in Sections 1 and 2 of this notebook, along with your freshly trained facial keypoint detector, you can now complete the full pipeline. That is given a color image containing a person or persons you can now
- Detect the faces in this image automatically using OpenCV
- Predict the facial keypoints in each face detected in the image
- Paint predicted keypoints on each face detected
In this Subsection you will do just this!
(IMPLEMENTATION) Facial Keypoints Detector¶
Use the OpenCV face detection functionality you built in previous Sections to expand the functionality of your keypoints detector to color images with arbitrary size. Your function should perform the following steps
- Accept a color image.
- Convert the image to grayscale.
- Detect and crop the face contained in the image.
- Locate the facial keypoints in the cropped image.
- Overlay the facial keypoints in the original (color, uncropped) image.
Note: step 4 can be the trickiest because remember your convolutional network is only trained to detect facial keypoints in $96 \times 96$ grayscale images where each pixel was normalized to lie in the interval $[0,1]$, and remember that each facial keypoint was normalized during training to the interval $[-1,1]$. This means - practically speaking - to paint detected keypoints onto a test face you need to perform this same pre-processing to your candidate face - that is after detecting it you should resize it to $96 \times 96$ and normalize its values before feeding it into your facial keypoint detector. To be shown correctly on the original image the output keypoints from your detector then need to be shifted and re-normalized from the interval $[-1,1]$ to the width and height of your detected face.
When complete you should be able to produce example images like the one below
In [35]:
# Load in color image for face detection
image = cv2.imread('images/obamas4.jpg')
# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# plot our image
fig = plt.figure(figsize = (9,9))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('image copy')
ax1.imshow(image)
Out[35]:
<matplotlib.image.AxesImage at 0x7fd655299588>
In [39]:
### TODO: Use the face detection code we saw in Section 1 with your trained conv-net
## TODO : Paint the predicted keypoints on the test image
from keras.models import load_model
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')
# Detect the faces in image
faces = face_cascade.detectMultiScale(gray, 1.1, 6) # scale factor 4 doesnt detect faces, for whatever reason
print(len(faces))
# Make a copy of the orginal image to draw face detections on
image_with_predicted_keypoints = np.copy(image)
model = load_model('my_model_Adam.h5')
for (x,y,w,h) in faces:
cv2.rectangle(image_with_predicted_keypoints, (x,y), (x+w,y+h), (255,0,0), 3)
# Crop and resize the face
face_image = gray[y:y+h , x:x+w]
resize_face = cv2.resize(face_image, (96, 96))
normalized_face = cv2.normalize(resize_face, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32FC1)
normalized_face = normalized_face[np.newaxis, :, :, np.newaxis] # add extra dimensions
keypoints = model.predict(normalized_face)
keypoints = keypoints * 48 + 48
kpx_coords_list = keypoints[0][0::2]
kpy_coords_list = keypoints[0][1::2]
kpx_coords_list = x + kpx_coords_list * w / 96
kpy_coords_list = y + kpy_coords_list * h / 96
for kx, ky in zip(kpx_coords_list, kpy_coords_list):
cv2.circle(image_with_predicted_keypoints, (kx, ky), 1, (0, 255, 0), 3)
# Display the image with the keypoints
fig = plt.figure(figsize = (9,9))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Image with predicted keypoints')
ax1.imshow(image_with_predicted_keypoints)
2
Out[39]:
<matplotlib.image.AxesImage at 0x7fd6329cae48>
(Optional) Further Directions - add a filter using facial keypoints to your laptop camera¶
Now you can add facial keypoint detection to your laptop camera - as illustrated in the gif below.
The next Python cell contains the basic laptop video camera function used in the previous optional video exercises. Combine it with the functionality you developed for keypoint detection and marking in the previous exercise and you should be good to go!
In [ ]:
import cv2
import time
from keras.models import load_model
def laptop_camera_go():
# Create instance of video capturer
cv2.namedWindow("face detection activated")
vc = cv2.VideoCapture(0)
# Try to get the first frame
if vc.isOpened():
rval, frame = vc.read()
else:
rval = False
# keep video stream open
while rval:
# plot image from camera with detections marked
cv2.imshow("face detection activated", frame)
# exit functionality - press any key to exit laptop video
key = cv2.waitKey(20)
if key > 0: # exit by pressing any key
# destroy windows
cv2.destroyAllWindows()
# hack from stack overflow for making sure window closes on osx --> https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv
for i in range (1,5):
cv2.waitKey(1)
return
# read next frame
time.sleep(0.05) # control framerate for computation - default 20 frames per sec
rval, frame = vc.read()
In [ ]:
# Run your keypoint face painter
laptop_camera_go()
(Optional) Further Directions - add a filter using facial keypoints¶
Using your freshly minted facial keypoint detector pipeline you can now do things like add fun filters to a person's face automatically. In this optional exercise you can play around with adding sunglasses automatically to each individual's face in an image as shown in a demonstration image below.
To produce this effect an image of a pair of sunglasses shown in the Python cell below.
In [ ]:
# Load in sunglasses image - note the usage of the special option
# cv2.IMREAD_UNCHANGED, this option is used because the sunglasses
# image has a 4th channel that allows us to control how transparent each pixel in the image is
sunglasses = cv2.imread("images/sunglasses_4.png", cv2.IMREAD_UNCHANGED)
# Plot the image
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.imshow(sunglasses)
ax1.axis('off');
This image is placed over each individual's face using the detected eye points to determine the location of the sunglasses, and eyebrow points to determine the size that the sunglasses should be for each person (one could also use the nose point to determine this).
Notice that this image actually has 4 channels, not just 3.
In [ ]:
# Print out the shape of the sunglasses image
print ('The sunglasses image has shape: ' + str(np.shape(sunglasses)))
It has the usual red, blue, and green channels any color image has, with the 4th channel representing the transparency level of each pixel in the image. Here's how the transparency channel works: the lower the value, the more transparent the pixel will become. The lower bound (completely transparent) is zero here, so any pixels set to 0 will not be seen.
This is how we can place this image of sunglasses on someone's face and still see the area around of their face where the sunglasses lie - because these pixels in the sunglasses image have been made completely transparent.
Lets check out the alpha channel of our sunglasses image in the next Python cell. Note because many of the pixels near the boundary are transparent we'll need to explicitly print out non-zero values if we want to see them.
In [ ]:
# Print out the sunglasses transparency (alpha) channel
alpha_channel = sunglasses[:,:,3]
print ('the alpha channel here looks like')
print (alpha_channel)
# Just to double check that there are indeed non-zero values
# Let's find and print out every value greater than zero
values = np.where(alpha_channel != 0)
print ('\n the non-zero values of the alpha channel look like')
print (values)
This means that when we place this sunglasses image on top of another image, we can use the transparency channel as a filter to tell us which pixels to overlay on a new image (only the non-transparent ones with values greater than zero).
One last thing: it's helpful to understand which keypoint belongs to the eyes, mouth, etc. So, in the image below, we also display the index of each facial keypoint directly on the image so that you can tell which keypoints are for the eyes, eyebrows, etc.
With this information, you're well on your way to completing this filtering task! See if you can place the sunglasses automatically on the individuals in the image loaded in / shown in the next Python cell.
In [ ]:
# Load in color image for face detection
image = cv2.imread('images/obamas4.jpg')
# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Plot the image
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Original Image')
ax1.imshow(image)
In [ ]:
## (Optional) TODO: Use the face detection code we saw in Section 1 with your trained conv-net to put
## sunglasses on the individuals in our test image
(Optional) Further Directions - add a filter using facial keypoints to your laptop camera¶
Now you can add the sunglasses filter to your laptop camera - as illustrated in the gif below.
The next Python cell contains the basic laptop video camera function used in the previous optional video exercises. Combine it with the functionality you developed for adding sunglasses to someone's face in the previous optional exercise and you should be good to go!
In [ ]:
import cv2
import time
from keras.models import load_model
import numpy as np
def laptop_camera_go():
# Create instance of video capturer
cv2.namedWindow("face detection activated")
vc = cv2.VideoCapture(0)
# try to get the first frame
if vc.isOpened():
rval, frame = vc.read()
else:
rval = False
# Keep video stream open
while rval:
# Plot image from camera with detections marked
cv2.imshow("face detection activated", frame)
# Exit functionality - press any key to exit laptop video
key = cv2.waitKey(20)
if key > 0: # exit by pressing any key
# Destroy windows
cv2.destroyAllWindows()
for i in range (1,5):
cv2.waitKey(1)
return
# Read next frame
time.sleep(0.05) # control framerate for computation - default 20 frames per sec
rval, frame = vc.read()
In [ ]:
# Load facial landmark detector model
model = load_model('my_model.h5')
# Run sunglasses painter
laptop_camera_go()